Benchmarks for normal cell identification

ABSTRACT

Provided herein are methods, compositions, and kits for determining cell signaling profiles in normal cells and comparing the cell signaling profiles of normal cells to cell signaling profiles from a test sample.

CROSS-REFERENCE

This application claims the benefit of U.S. Patent Application Nos.61/381,067 filed Sep. 8, 2010; 61/440,523 filed Feb. 8, 2011; 61/469,812filed Mar. 31, 2011; and 61/499,127 filed Jun. 20, 2011, which areincorporated by reference in their entireties.

BACKGROUND OF THE INVENTION

Personalized medicine seeks to provide prognoses, diagnoses and otheractionable medical information for an individual based on their profileof one or more biomarkers. Many of these diagnostics use classifierswhich are binary statistical models trained to identify biomarkers whichdifferentiate diseased cells from non-diseased cells (i.e., normalcells). While these classifiers are beneficial, a major drawback ofthese methods is that they only aim to determine similarity between twostates: disease and normal. Often, disease states are heterogeneous,which complicates the identification of biomarkers to distinguishdisease states and the development of these classifiers. For example, aclassifier may classify an individual as having a normal profile ascompared to one or more disease states even though the individualbiomarker profile is different from the biomarker profile observed innormal cells. This is referred to as a ‘false negative’ identification.In order to fully eliminate false negative identifications, theclassifier can model data representing all possible disease states.Since the heterogeneity of disease makes it difficult to obtain andcharacterize samples of all disease states, false negatives areinevitable.

Due to these limitations, in some instances it may be ideal to identifybiomarkers to allow for the determination of similarity between cellsfrom an individual and normal cells. Such a similarity comparison canbenefit from the development of a statistical model that cancharacterize and distinguish normal cell data.

SUMMARY OF THE INVENTION

In general, in one aspect, a method is provided comprising: a)identifying an activation level of one or more activatable elements in afirst cell-type from a test sample; b) identifying an activation levelof the one or more activatable elements in a second cell-type from atest sample; and c) determining a similarity value based on steps a) andstep b) and a statistical model, wherein the statistical model specifiesa range of activation levels of one or more activatable elements in thefirst cell-type and the second cell-type in a plurality of normalsamples, wherein the statistical model further specifies the variance ofthe activation levels of the one or more activatable elements associatedwith cells in the plurality of normal samples. In one embodiment,identifying the activation level of the one or more activatablecomprises: d) identifying the activation level of the one or moreactivatable elements in single cells derived from the test sample; e)identifying one or more cell-type markers in single cells derived fromthe test sample; and f) gating discrete populations of single cellsbased on the one or more cell-type markers associated with the singlecells. In another embodiment, the method further comprises generatingthe statistical model, wherein generating the statistical modelcomprises: d) identifying the activation level of the one or moreactivatable elements in single cells derived from the plurality ofnormal samples; e) identifying one or more cell-type markers in singlecells derived from the plurality of normal samples; f) gating cells inthe plurality of normal samples based on the one or more cell-typemarkers associated with the single cells; and g) generating thestatistical model that specifies the range of activation levelsassociated with cells in the normal samples.

In another embodiment, the statistical model further specifies thevariance of activation levels of the one or more activatable elementsassociated cells in the plurality of normal samples. In anotherembodiment, the one or more activatable elements are selected from thegroup consisting of: pStat1, pStat3, pStat4, pStat5, pStat6 and p-p38.In another embodiment, the method further comprises contacting the testsample and the plurality of normal samples with one or more modulators.In another embodiment, the one or more modulators is selected from thegroup consisting of: G-CSM, EPO, GM-CSF, IFNa and IL-6.

In another embodiment, the test sample and the plurality of normalsamples are derived from individuals with the same race, ethnicity,gender, or are in the same age-range. In another embodiment, the methodfurther comprises normalizing the activation level of the one or moreactivatable elements in the first cell-type and the second cell-typebased on a sample characteristic. In another embodiment, the samplecharacteristic comprises race, ethnicity, gender or age. In anotherembodiment, the identifying the activation level of the one or moreactivatable elements comprises flow cytometry. In another embodiment,the one or more activatable elements comprise one or more activatableelements from the plurality of normal samples that display variance ofless than 50% of the activation level of the one or more activatableelement in response to a modulator. In another embodiment, thesimilarity value is determined with a correlation metric or a fittingmetric.

In another embodiment, the method further comprises displaying theactivation level of the one or more activatable elements from the testsample and the plurality of normal samples in a report. In anotherembodiment, the displaying comprises a scatterplot, a line graph witherror bars, a histogram, a bar and whisker plot, a circle plot, a radarplot, a heat map, and/or a bar graph.

In another embodiment, the method further comprises making a clinicaldecision based on the similarity value. In another embodiment, theclinical decision comprises a diagnosis, prognosis, or monitoring asubject from whom the test sample was derived.

In another embodiment, the one or more activatable elements comprisesone or more proteins. In another embodiment, the identifying theactivation level of the one or more activatable elements comprisescontacting the one or more activatable elements with one or more bindingelements. In another embodiment, the one or more binding elementscomprises one or more phospho-specific antibodies. In anotherembodiment, the determining comprises use of a computer.

In another embodiment, the method further comprises administering atherapeutic agent to a subject from whom the test sample is derivedbased on the similarity value. In another embodiment, the method furthercomprises predicting a status of a second activatable element in asingle cell from the test sample, wherein the second activatable elementis different from the one or more activatable elements.

In another aspect, a method is provided comprising: a) identifying anactivation level of two or more activatable elements in single cellsfrom a test sample; b) obtaining a statistical model which specifies arange of activation levels of two more activatable elements in singlecells in a plurality of samples used as a standard; and c) determining asimilarity value between the activation levels in the single cells froma test sample and the statistical model. In one embodiment, thestatistical model further specifies the variance of activation levels ofthe one or more activatable elements in single cells in the plurality ofsamples used as a standard. In another embodiment, the one or moreactivatable elements are selected from the group consisting of: pStat1,pStat3, pStat4, pStat5, pStat6 and p-p38. In another embodiment, themethod further comprises contacting the test sample with one or moremodulators. In another embodiment, the one or more modulators isselected from the group consisting of: G-CSM, EPO, GM-CSF, IL-27, IFNaand IL-6. In another embodiment, the test sample and the plurality ofsamples used as a standard are derived from individuals with the samerace, ethnicity, gender, or are in the same age-range. In anotherembodiment, the method further comprises normalizing the activationlevel of the two or more activatable elements in single cells from thetest sample based on a sample characteristic. In another embodiment, thesample characteristic comprises race, ethnicity, gender or age. Inanother embodiment, the identifying the activation level of the one ormore activatable elements comprises flow cytometry. In anotherembodiment, the two or more activatable elements comprise one or moreactivatable elements from the plurality of samples used as a standardthat display variance of less than 50% of the activation level of theone or more activatable elements in response to a modulator. In anotherembodiment, the similarity value is determined with a correlation metricor a fitting metric. In another embodiment, the method further comprisesdisplaying the activation level of one or more of the two or moreactivatable elements from the test sample and the plurality of samplesused as a standard in a report.

In another embodiment, the displaying comprises a scatterplot, a linegraph with error bars, a histogram, a bar and whisker plot, a circleplot, a radar plot, a heat map, and/or a bar graph. In anotherembodiment, the method further comprises making a clinical decisionbased on the similarity value. In another embodiment, the clinicaldecision comprises a diagnosis, prognosis, or monitoring a subject fromwhom the test sample was derived. In another embodiment, the methodfurther comprises administering a therapeutic agent to a subject fromwhom the test sample is derived based on the similarity value. Inanother embodiment, the method further comprises predicting the statusof a second activatable element in a single cell from the test sample,wherein the second activatable element is different from the two or moreactivatable elements.

In another embodiment, the two or more activatable elements comprise twoor more proteins. In another embodiment, the identifying the activationlevel of the two or more activatable elements comprises contacting thetwo or more activatable elements with one or more binding elements. Inanother embodiment, the one or more binding elements comprises one ormore phosphospecific antibodies. In another embodiment, the determiningcomprises use of a computer.

In another aspect, a method of generating a normal cell profile isprovided comprising obtaining a plurality of samples of cells fromnormal individuals, contacting the plurality of samples of cells fromthe normal individuals with one or more modulators, measuring anactivation level of one or more activatable elements in the plurality ofsamples from the normal individuals, and generating a profile, whereinthe profile comprises one or more ranges of the activation level of theone or more activatable elements from the plurality of samples of cellsfrom the normal individuals.

In another embodiment, the profile comprises one or more ranges ofactivation levels of the one or more activatable elements that exhibitvariance of less than 50% among normal samples. In another embodiment,the method further comprises gating each of the plurality of samples ofcells from normal individuals into separate populations of cells. Inanother embodiment, the gating is based on cell surface markers. Inanother embodiment, the contacting comprises contacting the cells with aplurality of concentrations of the one or more modulators. In anotherembodiment, the measuring comprises measuring the activation level ofthe one or more activatable elements over a series of timepoints.

In another embodiment, the normal individuals have the same gender, raceor ethnicity. In another embodiment, the normal individuals are selectedbased on the age of the normal individuals.

In another embodiment, the measuring the activation level of one or moreactivatable elements comprises flow cytometry. In another embodiment,the method further comprises displaying the activation level of the oneor more activatable elements from the plurality of samples of cells fromnormal individuals in a report. In another embodiment, the displayingcomprises a scatterplot, a line graph with error bars, a histogram, abar and whisker plot, a circle plot, a radar plot, a heat map, and/or abar graph.

In another embodiment, the one or more activatable elements comprisesone or more proteins. In another embodiment, the measuring theactivation level of the one or more activatable elements comprisescontacting the one or more activatable elements with one or more bindingelements. In another embodiment, the one or more binding elementscomprises one or more phospho-specific antibodies. In anotherembodiment, the one or more activatable elements are selected from thegroup consisting of: pStat1, pStat3, pStat4, pStat5, pStat6 and p-p38.In another embodiment, the one or more modulators is selected from thegroup consisting of: G-CSM, EPO, GM-CSF, IL-27, IFNa and IL-6.

In another aspect, a method is provided comprising: a) measuring anactivation level of one or more activatable elements from cells from atest sample from a subject; b) comparing the activation level of the oneor more activatable elements from cells from the test sample to a model,wherein the model is derived from determining a range of activationlevels of one or more activatable elements from samples of cells from aplurality of normal individuals; and c) preparing a report displayingthe activation level of the one or activatable elements from the samplesof cells from the plurality of normal individuals to the activationlevel of the one or more activatable elements from cells from the testsample from the subject.

In one embodiment, the samples of cells from the plurality of normalindividuals were gated to separate populations of cells. In anotherembodiment, the method further comprises gating the sample of cells fromthe test sample from the subject into separate populations of cells. Inanother embodiment, the gating is based on one or more cell surfacemarkers. In another embodiment, the samples of cells from a plurality ofnormal individuals were contacted with one or more modulators. Inanother embodiment, the method further comprises contacting theplurality of samples of cells from the test sample from the subject withthe one or more modulators. In another embodiment, the normalindividuals and the subject have the same gender, race, or ethnicity. Inanother embodiment, the method further comprises normalizing theactivation level of the one or more activatable elements from cells formthe test sample based on a sample characteristic. In another embodiment,the sample characteristic comprises race, ethnicity, gender or age. Inanother embodiment, the normal individuals are selected based on the ageof the test subject. In another embodiment, the measuring the activationlevel of the one or more activatable elements comprises flow cytometry.In another embodiment, the displaying comprises a scatterplot, a linegraph with error bars, a histogram, a bar and whisker plot, a circleplot, a radar plot, a heat map, and/or a bar graph. In anotherembodiment, the one or more activatable elements comprises one or moreproteins. In another embodiment, the measuring an activation level ofone or more activatable elements comprises contacting the one or moreactivatable elements with one or more binding elements. In anotherembodiment, the one or more binding elements comprises one or morephospho-specific antibodies. In another embodiment, the one or moreactivatable elements are selected from the group consisting of: pStat1,pStat3, pStat4, pStat5, pStat6 and p-p38. In another embodiment, the oneor more modulators is selected from the group consisting of G-CSM, EPO,GM-CSF, IL-27, IFNa and IL-6.

In another embodiment, the method further comprises making a clinicaldecision based on said comparing. In another embodiment, the clinicaldecision comprises a diagnosis, prognosis, or monitoring the subject. Inanother embodiment, the method further comprises providing the report toa healthcare provider. In another embodiment, the method furthercomprises providing the report to the subject. In another embodiment,the report comprises information on cell growth, cell survival and/orcytostasis.

In another aspect, a report comprising a visual representation ofmultiparametric results of a test sample is provided, the visualrepresentation comprising a comparison between an activation level oftwo or more activatable elements in single cells from a test sample anda range of activation levels of the two or more activatable elements insingle cells in a plurality of samples used as a standard. In oneembodiment, the report further comprises a statistical model, whereinthe statistical model specifies the range of activation levels of thetwo or more activatable elements in single cells in a plurality ofsamples used as a standard. In another embodiment, the report furthercomprises a similarity value between the activation level of the two ormore activatable elements in single cells from a test sample and thestatistical model. In another embodiment, the report further comprises ascatterplot, a line graph with error bars, a histogram, a bar andwhisker plot, a circle plot, a radar plot, a heat map, and/or a bargraph. In another embodiment, a computer server generates the report. Inanother embodiment, the report comprises information on cell growth,cell survival and/or cytostasis. In another embodiment, the two or moreactivatable elements comprise two or more proteins.

In another aspect, a method of preparing a report is provided comprisinga) determining levels of two or more activatable elements in singlecells obtained from a subject; b) comparing the levels of the two ormore activatable elements to levels of the two or more activatableelements from a plurality of samples used as a standard; and c)preparing a report displaying the comparison. In one embodiment, thedisplaying comprises a scatterplot, a line graph with error bars, ahistogram, a bar and whisker plot, a circle plot, a radar plot, a heatmap, and/or a bar graph. In another embodiment, a computer servergenerates the report. In another embodiment, the report comprisesinformation on cell growth, cell survival and/or cytostasis. In anotherembodiment, the two or more activatable elements comprise two or moreproteins.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 shows boxplots for a range of signaling for each node in eachpopulation.

FIG. 2 illustrates some of the various cell-subpopulations which can befound in blood. For example, Naïve Helper T cells can be asub-population of Helper T cells, T Cells, and Lymphocytes and can bedistinct from Memory Cytotoxic T or Monocytes by their cell surfacemarkers. Once the cell sub-populations are determined for each sample,the range of signaling of activatible elements can be statisticallydescribed. Note that the range of signaling for the particularactivatible elements IFNa2.p-Stat1 and IL-6.p-Stat1 are differentbetween Monocytes, Naïve Helper T cells, and Memory Cytotoxic T cells.These ranges of signaling which define normality within each cellpopulation can then be quantified statistically, and disease state for aparticular patient can be determined by comparison to these normalranges of signaling.

FIG. 3 shows a schematic of an experiment for characterizing signaltransduction networks implicated in the growth and survival of AMLcells.

FIG. 4 shows that FLT3-ITD AML with high mutational load responses aremore homogenous than FLT3-WT AML.

FIG. 5 shows that FLT3 WT donors are more heterogeneous than FLT3 ITDdonors and show distinct patterns.

FIG. 6A shows signaling ranges for nodes within naïve cytotoxic T cellsfor D1 (darker boxplots) and D2 samples (lighter boxplots). FIG. 6 alsoshows cytokine signaling responses within the naïve cytototoxic T subsetwith significant age-associations in both datasets.

FIG. 7 shows (A) αIgD induced p-S6 signaling (based on the log₂foldincrease in MFI in αIgD treated cells relative to the untreated control(0 min)) over time are shown for the African American (AA) and EuropeanAmerican (EA) donors. The difference in p-S6 signaling (averaged overtime points) between racial groups is statistically significant. (B) Thepercentage of CD20+ B cells that were IgD+ is shown for the AA and EAdonors. The difference in IgD+ frequency between racial groups isstatistically significant. In both (A) and (B), one of the ten donorswas excluded due to an insufficient number (<200) of B cells collectedfor analysis.

FIG. 8 shows an embodiment of a cell signaling report. FIG. 8A is anoverview of the report, and FIGS. 8B, 8C, 8D, 8E, and 8F show details ofthe report.

FIG. 9 shows another embodiment of a cell signaling report. FIGS. 9A,9B, 9C, 9D, and 9E show different parts of the report.

FIG. 10A shows an overview of another embodiment of a cell signalingreport. FIG. 10 shows signaling data: Stimulation time is 5-15 minutes.Kinase inhibitors when used were incubated on cells for 1 hr prior tostimulation. Radar plot axis is on a Log 2 scale. Cell growth assay:Cells were grown with the indicated conditions for 48 hours tocharacterize the dependence or independence on selected growth factorsfor cell survival and proliferation. Apoptosis/Cytostasis: After 48 hrsof growth phase in growth factors (FL, TPO, SCF, IL3), cells wereincubated with drugs for 48 hrs. Abbreviations: p-, phospho; ERK,extracellular-signal-regulated kinase; S6, S6 Ribosome; STAT, SignalTransducers and Activators of Transcription; FL, FLT3 ligand; SCF, StemCell Factor; TPO, Thrombopoietin; TMZ, tomozolomide; AraC, cytarabine;K.I., kinase inhibitor; Topo. II, Topoisomerase II; HDAC, histonedeacetylase; DNMT, DNA methyltransferase; GFs, growth factors; PARP,Poly (ADP-ribose) polymerase; JAK, Janus Kinase; MEK, Mitogen-activatedprotein kinase; PI3K, Phosphatidylinositol 3-kinase; mTor, mammaliantarget of rapamycin; HSP90, Heat Shock Protein 90. FIGS. 10B, 10C, 10D,10E, 10F, 10G, 10H, 10I, 10J, and 10K show details of the report.

FIG. 11 shows normal PMBC DNA damage kinetics to double strand breaksinduced by etoposide, Ara-C/Daunorubicin, and Mylotarg.

FIG. 12 shows normal PBMC Myeloid DNA Damage Kinetics to Double StrandBreaks induced by Etoposide, Ara-C/Daunorubicin, or Mylotarg.

FIG. 13 shows normal PBMC Lymph and Myeloid response toAra-C/Daunorubicin: (kinetics and effect of Daunorubicin dose) measuringDNA Damage Response and Daunorubicin fluorescence.

FIG. 14 shows that AML samples can display a range of DDR responsescompared to Normal Healthy Non-Diseased CD34+ Myeloblasts.

FIG. 15 shows SCNP results in healthy controls and MDS patients.

FIG. 16 illustrates a networked system for the remote acquisition oranalysis of data obtained using methods described herein.

DETAILED DESCRIPTION OF THE INVENTION

The methods, compositions, and kits disclosed herein incorporateinformation disclosed in other applications and texts. The followingpatent and other publications are hereby incorporated by reference intheir entireties: Haskell et al, Cancer Treatment, 5^(th) Ed., W.B.Saunders and Co., 2001; Alberts et al., The Cell, 4^(th) Ed., GarlandScience, 2002; Vogelstein and Kinzler, The Genetic Basis of HumanCancer, 2d Ed., McGraw Hill, 2002; Michael, Biochemical Pathways, JohnWiley and Sons, 1999; Weinberg, The Biology of Cancer, 2007;Immunobiology, Janeway et al. 7^(th) Ed., Garland, and Leroith andBondy, Growth Factors and Cytokines in Health and Disease, A MultiVolume Treatise, Volumes 1A and 1B, Growth Factors, 1996. Otherconventional techniques and descriptions can be found in standardlaboratory manuals such as Genome Analysis: A Laboratory Manual Series(Vols. I-IV), Using Antibodies: A Laboratory Manual, Cells: A LaboratoryManual, PCR Primer: A Laboratory Manual, and Molecular Cloning: ALaboratory Manual (all from Cold Spring Harbor Laboratory Press),Stryer, L. (1995) Biochemistry (4th Ed.) Freeman, New York, Gait,“Oligonucleotide Synthesis: A Practical Approach” 1984, IRL Press,London, Nelson and Cox (2000), Lehninger, Principles of Biochemistry 3rdEd., W. H. Freeman Pub., New York, N.Y. and Berg et al. (2002)Biochemistry, 5th Ed., W. H. Freeman Pub., New York, N.Y.; and Sambrook,Fritsche and Maniatis. “Molecular Cloning A laboratory Manual” 3rd Ed.Cold Spring Harbor Press (2001), all of which are herein incorporated intheir entirety by reference for all purposes.

Patents and applications that are also incorporated by reference intheir entirety include U.S. Pat. Nos. 7,381,535, 7,393,656, 7,695,924and 7,695,926 and U.S. patent application Ser. Nos. 10/193,462;11/655,785; 11/655,789; 11/655,821; 11/338,957; 12/877,998; 12/784,478;12/730,170; 12/703,741; 12/687,873; 12/617,438; 12/606,869; 12/713,165;12/293,081; 12/581,536; 12/776,349; 12/538,643; 12/501,274; 61/079,537;12/501,295; 12/688,851; 12/471,158; 12/910,769; 12/460,029; 12/432,239;12/432,720; 12/229,476, 12/877,998; 13/083,156; 61/469,812; 61/436,534;61/317,187; and 61/353,155; and PCT Application Nos. PCT/US2011/029845and PCT/US2010/048181.

Some commercial reagents, protocols, software and instruments that areuseful in some embodiments are available at the Becton Dickinson Websitehttp://www.bdbiosciences.com/features/products/, and the Beckman Coulterwebsite, http://www.beckmancoulter.com/Default.asp?bhfv=7. Relevantarticles include High-content single-cell drug screening withphosphospecific flow cytometry, Krutzik et al., Nature Chemical Biology,23 Dec. 2007; Irish et al., FLt3 ligand Y591 duplication and Bcl-2 overexpression are detected in acute myeloid leukemia cells with high levelsof phosphorylated wild-type p53, Neoplasia, 2007; Irish et al. Mappingnormal and cancer cell signaling networks: towards single-cellproteomics, Nature, Vol. 6 146-155, 2006; Irish et al., Single cellprofiling of potentiated phospho-protein networks in cancer cells, Cell,Vol. 118, 1-20 Jul. 23, 2004; Schulz, K. R., et al., Single-cellphospho-protein analysis by flow cytometry, Curr Protoc Immunol, 2007,78:8 8.17.1-20; Krutzik, P. O., et al., Coordinate analysis of murineimmune cell surface markers and intracellular phosphoproteins by flowcytometry, J Immunol. 2005 Aug. 15; 175(4):2357-65; Krutzik, P. O., etal., Characterization of the murine immunological signaling network withphosphospecific flow cytometry, J Immunol. 2005 Aug. 15; 175(4):2366-73;Shulz et al., Current Protocols in Immunology 2007, 78:8.17.1-20;Stelzer et al. Use of Multiparameter Flow Cytometry andImmunophenotyping for the Diagnosis and Classification of Acute MyeloidLeukemia, Immunophenotyping, Wiley, 2000; and Krutzik, P. O. and Nolan,G. P., Intracellular phospho-protein staining techniques for flowcytometry: monitoring single cell signaling events, Cytometry A. 2003October; 55(2):61-70; Hanahan D., Weinberg, The Hallmarks of Cancer,CELL, 2000 Jan. 7; 100(1) 57-70; Krutzik et al, High content single celldrug screening with phophosphospecific flow cytometry, Nat Chem Biol.2008 February; 4(2):132-42. Experimental and process protocols and otherhelpful information can be found at http:/proteomices.stanford.edu. Thearticles and other references cited below are also incorporated byreference in their entireties for all purposes.

One embodiment described herein is a method for identifying ranges ofactivatable elements in different cell populations which can be used tocharacterize normal single cells. “Normal cells” or “healthy cells,” asreferred to herein, can be cells that are not associated with anydisease or pre-disease state. Normal cells or healthy cells can be usedas a standard. Examples of activatable elements are described in detailbelow in the section entitled “Activatable Elements.” In someembodiments outlined in the examples below, the activatable elements areproteins that are phosphorylated in cell signaling pathways. In oneembodiment, signaling response is measured based on the activation levelor phosphorylation of the proteins involved in signaling pathways. Othertypes of activatable elements can be used to characterize normal singlecells.

Normal can include the concept of a standard, which may be diseasedstate. A test sample can be compared to a standard. A parameter of atest sample, e.g., an activation level of an activatable element, can beadjusted or normalized based on a standard. A similarity value can beadjusted or normalized based on a standard.

In one embodiment, the observed activation levels of the activatableelements are induced by contacting the cells with one or more modulators(referred to herein as “stimulating the cells”). Modulators can becompounds or proteins that effect cell signaling. The cells can becontacted with different concentrations of one or more modulators toinduce activation of the activatable elements. The amount by which theactivatable element is induced by a modulator is referred to herein asits activation level. In one embodiment in the examples discussed below,the one or more modulators are used to induce phosphorylation of theactivatable elements. In other embodiments one or more modulators may beused to induce other types of conformational or physical changes inactivation elements. In the embodiments shown in the examples below theactivation level of the activatable elements is characterized in singlecells using multi-parametric flow cytometry. In other embodiments, othertypes of technology used to characterize activatable elements in singlecells may be used (e.g., mass spectrometry, mass spectrometry-based flowcytometry). Some of these technologies are described below in thesection entitled “Detection.”

The term “node” is used herein to describe a specificmodulator/activatable element pair. Nodes can be represented using thenotation modulator->activatable element. For example, IL-6->pStat5represents the modulator IL-6 and the activatable element pStat5.

Characterization of activatable levels in normal single cells can havemany benefits. First, understanding the range of activation levels innormal cells can provide valuable insight into the physiology of healthycells, specifically the mechanisms by which healthy cells controlsignaling response(s). Second, establishing ranges of modulator-inducedactivation levels can allow for the identification of modulator-inducedactivation levels that are tightly controlled in healthy cells andtherefore demonstrate little variance in healthy cells. The variance inactivation level of an activatable element between two or more samplescan be about, or less than about, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100%. Thefold difference in variance in activation level of an activatableelement between two or more samples can be about, or less than about,1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or20-fold.

Different concentrations of modulators can be used to elicit differentinduced activation levels in healthy cells. Further, the activationlevels induced by the modulators may be measured in single cells atdifferent time points after modulation of the cells. Measuring theactivation levels following modulation over time is discussed below inthe section below entitled “Generation of Dynamic Activation StateData.” Measuring activation levels of nodes at different time points andusing different concentrations of modulators can be beneficial as it canallow for a finer-resolution observation of the different activationresponses of the cells to the modulators. As discussed with respect tothe examples below, different concentrations of modulators can producedistinct activation levels at different time points. This resolution canallow for the identification of time points and/or concentrations ofmodulators that exhibit little variance and the observed ranges ofactivation levels can be used to distinguish and characterize normalcells.

Additionally, modeling the dynamic response of nodes over time canprovide additional metrics that can be used to characterize the cellsbased on the activation levels over time (referred to herein as the“activation profile” of a node). For example, the activation profile maybe used to generate metrics such as slope or can be expressed usinglinear equations. These metrics may also be used to characterize anddistinguish normal single cells.

In some embodiments discussed below, the benefits of characterizing theranges of activation levels in normal single cells are further enhancedby the segregation of single cells into discrete cell populations. Acell population can be a set of cells that share a common characteristicincluding but not limited to: cell type, cell morphology and expressionof a gene or protein. Some analytical methods, such as multi-parametricflow cytometry, not only allow for the simultaneous measurement ofactivation levels of several activatable elements in single cells, butalso allow for the measurement of other markers (e.g., cell surfaceproteins, activatable elements) that can be used to determine a type ofthe cell. These markers can be used in conjunction with gating methods(described below in the section entitled “Computational Identificationof Cell Populations”) to segregate single cells into discrete cellsub-populations prior to analyzing the activation state data associatedwith the single cells.

Once these cell sub-populations are identified the ranges of signalingof activatible elements can be quantified within each cellsub-population. The signaling ranges within each sub-population can thenbe described for normal and diseased states by statistical methods suchas histograms, boxplots or otherwise. Multivariate statistical methods,such as regression, random forests, or clustering, may also be used tosummarize the ranges of signaling across all cell sub-populations fornormal and diseased states (See e.g., FIG. 2).

Cell signaling information for a subject, e.g., a patient, can benormalized based on a sample grouping or characteristic of the subject,e.g., race, gender, age, or ethnicity. The cell signaling informationcan be an activation level of one or more activation elements.

As demonstrated by the examples below, different cell populationsexhibit different activation responses to modulators. By furthersegregating the cells based on the cell population, modulator-inducedactivation levels that distinguish and characterize normal cells canfurther be refined.

One embodiment of the invention is directed to methods for determiningthe status of an individual by determining an activation level of one ormore activatable elements of cells in different discrete populations ofcells obtained from the individual. Typically, the status of anindividual can be a status related to the health of the individual(referred to herein as “health status” or “disease status”), but anytype of status can be determined if it can be correlated to the statusof cells (e.g., single cells) from one or more discrete populations ofcells from the individual. In some embodiments, provided herein aremethods for determining the status of an individual by creating aresponse panel using two or more discrete cell populations. In someembodiments, the status of an individual is determined by a methodcomprising: a) contacting a first cell from a first discrete cellpopulation from said individual with at least a first modulator; b)contacting a second cell from a second discrete cell population fromsaid individual with at least a second modulator; d) determining anactivation level of at least one activatable element in said first celland said second cell; e) creating a response panel for said individualcomprising said determined activation levels of said activatableelements; and f) making a decision regarding the status of saidindividual, wherein said decision is based on said response panel.

Thus, provided herein are methods for the determination of the status ofan individual by analyzing a plurality (e.g., two or more) of discretepopulations of cells. In some embodiments, provided herein are methodsto demarcate discrete populations of cells that correlate with aclinical outcome for a disease. In some embodiments, the methodsprovided herein use different discrete populations of cells, theanalysis of which, in combination, allows for the determination of astatus of an individual. In some embodiments, the methods providedherein use different discrete populations of cells the analysis ofwhich, in combination, allows for the determination of the state of acellular network. In some embodiments, provided herein are methods forthe determination of a causal association between discrete populationsof cells, where the causal association is indicative of the status of acell network. In another embodiment, provided herein are methods todetermine whether one or more cell populations that are part of acellular network are associated with a status.

The status of an individual can be associated with a diagnosis,prognosis, choice or modification of treatment, and/or monitoring of adisease, disorder, or condition. Through the determination of the statusof an individual, a health care practitioner can assess whether theindividual is in the normal range for a particular condition or whetherthe individual has a pre-pathological or pathological conditionwarranting monitoring and/or treatment. Thus, in some embodiments, thestatus of an individual involves the classification, diagnosis,prognosis of a condition or outcome after administering a therapeutic totreat the condition.

One embodiment of the methods provided herein involves theclassification, diagnosis, prognosis of a condition or outcome afteradministering a therapeutic to treat the condition. Another embodimentof the methods described herein involves monitoring and predicting anoutcome of a condition. Another embodiment is drug screening using someof the methods described herein to determine which drugs may be usefulin particular conditions. In some embodiments, an analysis methodinvolves evaluating cell signals and/or expression markers in differentdiscrete cell populations in performing these processes. One embodimentof cell signal analysis involves the analysis of one or morephosphorylated proteins (e.g., by flow cytometry) in different discretecell populations. The classification, diagnosis, prognosis of acondition and/or outcome after administering a therapeutic to treat thecondition is then determined based in the analysis of the one or morephosphorylated proteins in different discrete cell populations. In oneembodiment, a signal transduction-based classification of a conditioncan be performed using clustering of phospho-protein patterns orbiosignatures of the different cell discrete populations.

In some embodiments, a treatment is chosen based on a characterizationof a plurality of discrete cell populations. In some embodiments,characterizing a plurality of discrete cell populations comprisesdetermining the activation state of one or more activatable elements inthe plurality of cell populations. The activatable element(s) analyzedamong the plurality of discrete cell populations can be the same or canbe different.

In some embodiments, provided herein are methods for classification,diagnosis, prognosis of a condition or outcome after administering atherapeutic to treat the condition by characterizing one or morepathways in different discrete cell populations. In some embodiments, atreatment is chosen based on the characterization of the pathway(s)simultaneously in the different discrete cell populations. In someembodiments, characterizing one or more pathways in different discretecell populations comprises determining whether apoptosis pathways, cellcycle pathways, signaling pathways, or DNA damage pathways arefunctional in the different discrete cell populations based on theactivation levels of one or more activatable elements within thepathways, where a pathway is functional if it is permissive for aresponse to a treatment.

In some embodiments, the characterization of different discrete cellpopulations in a condition (e.g., cancer) shows disruptions in cellularnetworks that are reflective of increased proliferation, increasedsurvival, evasion of apoptosis, insensitivity to anti-growth signals andother mechanisms. In some embodiments, the disruption in these networkscan be revealed by exposing a plurality of discrete cell populations toone or more modulators that mimic one or more environmental cues. Forexample, without intending to be limited to any theory, severaldifferent cell types participate as part of the immune system, includingB cells, T cells, macrophages, neutrophils, basophils and eosinophils.Each of these cell types has a distinct role in the immune system, andcommunicates with other immune cells using secreted factors calledcytokines, including interleukins, TNF, and the interferons. Macrophagesphagocytose foreign bodies and are antigen-presenting cells, usingcytokines to stimulate specific antigen dependent responses by B and Tcells and non-specific responses by other cell types. T cells secrete avariety of factors to coordinate and stimulate immune responses tospecific antigen, such as the role of helper T cells in B cellactivation in response to antigen. The proliferation and activation ofeosinophils, neutrophils and basophils respond to cytokines as well.Cytokine communication is often local, within a tissue or between cellsin close proximity. Each of the cytokines is secreted by one set ofcells and provokes a response in another target set of cells, oftenincluding the cell that secretes the cytokine.

In response to tissue injury, a multifactorial network of chemicalsignals can initiate and maintain a host response designed to heal theafflicted tissue. When a condition such as cancer is present in anindividual the homeostasis in, e.g., tissue, organ and/ormicroenvironment is perturbed. For example, neoplasia-associatedangiogenesis and lymphangiogenesis produces a chaotic vascularorganization of blood vessels and lymphatics where neoplastic cellsinteract with other cell types (mesenchymal, haematopoietic andlymphoid) and a remodelled extracellular matrix. Neoplastic cellsproduce an array of cytokines and chemokines that are mitogenic and/orchemoattractants for granulocytes, mast cells, monocytes/macrophages,fibroblasts and endothelial cells. In addition, activated fibroblastsand infiltrating inflammatory cells can secrete proteolytic enzymes,cytokines and chemokines, which can be mitogenic for neoplastic cells,as well as endothelial cells involved in neoangiogenesis andlymphangiogenesis. These factors can potentiate tumor growth, stimulateangiogenesis, induce fibroblast migration and maturation, and enablemetastatic spread via engagement with either the venous or lymphaticnetworks. Thus, determining the activation state data of various cellpopulations in an individual can provide a better picture of the statusof the individual and/or the state of the cellular network.

In a condition like rheumatoid arthritis (RA), contributions made byinteractions between dendritic cells, T cells and other immune cells,and local production of cytokines and chemokines may contribute to thepathogenesis of RA. These cells can further interact with local cells(e.g., synoviocytes). In response to local inflammation and productionof proinflammatory cytokines, after unknown event dendritic cells, Tcells and other immune cells can be attracted to the synovium inresponse to local production of cytokines and chemokines. In somepatients with rheumatoid arthritis, chronic inflammation leads to thedestruction of the cartilage, bone, and ligaments, causing deformity ofthe joints. Damage to the joints can occur early in the disease and beprogressive.

The determination of the status (e.g., health status, disease statusand/or any status indicating the pathophysiology of an individual) mayalso indicate response of an individual to treatment for a condition.Such information can allow for ongoing monitoring of the conditionand/or additional treatment. In one embodiment, provided herein aremethods for the detection of the presence of disease-associated cells orthe absence or reduction of cells necessary for normal physiology in anindividual that is being treated, or was previously treated, for thedisease or condition. In some embodiments, the status may also indicatepredicted response to a treatment.

In some embodiments, the determination of the status of an individualmay be used to ascertain whether a previous condition or treatment hasinduced a new pre-pathological or pathological condition that requiresmonitoring and/or treatment. For example, treatment for many forms ofcancers (e.g., lymphomas and childhood leukemias) can induce certainadult leukemias, and the methods described herein can allow for theearly detection and treatment of such leukemias.

In a further embodiment, the status of an individual can indicate anindividual's immunologic status and can reflect a general immunologicstatus, an organ or tissue specific status, or a disease related status.

Cells respond to environmental and systemic signals to adjust theirresponses to varying demands. For example, cells respond to factors suchas hormones, growth factors and cytokine produced by other cells or fromthe environment. Cells also respond to injury and physiological changes.As a result, each tissue, organ, microenvironment (e.g., niche) or cellhas the capacity to modulate the activity of cells. In addition, thepresence of cells (e.g. cancer cells) can have influence in asurrounding tissue, organ, microenvironment (e.g., niche) or cell.

A cell might be passive in the communication with a surrounding tissue,organ, microenvironment (e.g., niche) or cell, merely adjusting theiractivity levels according to the environment demands. A cell mightinfluence a surrounding tissue, organ, microenvironment (e.g., niche) orcell by virtue of progeny or signals such as cell contacts, secreted ormembrane bounds factors. Thus, cells co-exist with other types of cellsin a complex environment milieu. Different types of cells that interactwith each other in a tissue, an organ, or a microenvironment such as aniche participate in a network that might determine the status of anindividual (e.g., developing of a condition or performing normalfunctions).

A discrete cell population, as used herein, can refer to a population ofcells in which the majority of cells is of a same cell type or has asame characteristic. For many years, research into several conditions(e.g., cancer) has focused on attempts to identify a causative cellpopulation comprised of cells of a single cell type. However, severaldiscrete cell populations or the interactions between several cellpopulations may contribute to the pathology of a condition. For example,in the case of a cancer cell, the cancer cell may possess a dysregulatedresponse to an environmental cue (e.g., cytokine) such that the cellproliferates rather than undergo apoptosis. Alternatively, theenvironment in which the cell is located (e.g. niche, tissue, organ) mayabnormally produce a factor that causes the cancer cell to undergouncontrolled proliferation. In addition, the cancer cell may produce oneor more factors that influence its environment (e.g. niche, tissue,organ), and, as a result the pathology of the cancer is worsened.

Thus, the successful diagnosis of a condition and use of therapies mayrequire knowledge of the activation state data of different discretecell populations that may play a role in the pathogenesis of a condition(e.g., cancer). The determination of the activation state data ofdifferent discrete cell populations that might interact directly orindirectly in a network serves as an indicator of the state of thenetwork. In addition, it provides directionality to the interactionsamong the different discrete cell populations in the network. It alsoprovides information across the cell populations participating in thenetwork. As a result, the determination of activation state data ofdifferent discrete cell populations can serve as an indicator of acondition.

In some embodiments, the activation state data of a plurality ofpopulations of cells is determined by analyzing multiple single cells ineach population (e.g. by flow cytometry). Measuring multiple singlecells in each discrete cell population in an individual providesmultiple data points that in turn allows for the determination of thenetwork boundaries in the individual. Measuring modulated networks at asingle cell level provides the lever of biologic resolution that allowsthe assessment of intrapatient clonal heterogeneity ultimately relevantto disease management and outcome. The network boundaries and/or thestate of the network might change when the individual is suffering froma pathological condition or if the individual is responding or notresponding to treatment. Thus, the determination of network boundariesand/or the state of the network can be used for diagnosis, prognosis ofa condition or determination of outcome after administering atherapeutic to treat the condition.

Provided herein are methods for determining the status of an individualby analyzing different discrete cell populations in said individual. Insome embodiments, provided herein are methods for determining the stateof a cellular network. The cellular network can be correlated with thestatus of an individual. In some embodiments, determining the status ofan individual involves the classification, diagnosis, prognosis of acondition or outcome after administering a therapeutic to treat thecondition.

The methods provided herein can be used to determine a range ofactivation levels of one or more activation elements. In someembodiments, the activation level of a first activatable elementcorrelates with the activation level of a second activatable element. Insome embodiments, the correlation is a positive correlation; in someembodiments, the correlation is a negative correlation. In someembodiments, an activation level of a plurality of activatable elementsis determined. In some embodiments, the activation level of a firstsubset of one or more activatable elements is determined in a testsample, and the activation level of a second subset of one or moreactivatable elements is predicted based on known correlations betweenthe first subset of one or more activatable elements and the secondsubset of activatable elements.

Generating a Statistical Model of Induced Activation in Normal Cells

In one embodiment, the methods described herein allow for theidentification of one or more activation levels that can be used tocharacterize normal cells. The one or more activation levels may be usedto generate a statistical model that can be used to determine whether acell associated with a test subject (e.g., an undiagnosed individual)exhibits a cell profile that is comparable to a profile for a normalcell.

Multiple methods can be used to determine the activation state of acell, but, in one specific embodiment, samples of normal cells aretreated with one or more modulators at a variety of differentconcentrations. The activation levels of a set of activation elementscan be measured at a number of pre-defined time intervals using flowcytometry or other comparable techniques for measuring activation levelsin single cells. In some embodiments, markers or their levels can beused to segregate the activation elements into discrete cellpopulations. The activation profiles for each cell population can beanalyzed to identify one or more ranges of activation levels thatexhibit little variance among the cell populations of normal samples.The activation profiles can be further analyzed to identify activationlevels associated with different time points and/or modulatorconcentrations that are unique to a population of cells. The activationprofiles can be further analyzed to identify slopes or other dynamiccharacteristics of the activation profiles that either exhibit littlevariance and/or are unique to a population of cells.

In some embodiments, activation state data (e.g., activation levelsand/or activation profiles) derived from the normal cells can be used todetermine the similarity between the normal cells and one or moresamples derived from test subjects (e.g., individuals with unknownmedical status; e.g., undiagnosed individuals). In these embodiments,the activatable elements from normal cells can be measured in a samplefrom a test subject (e.g., an undiagnosed individual).

In other embodiments, all activation state data derived from the normalsamples is used to generate a statistical model including the range ofobserved activation levels in normal cells and the associated variance.The activation state data for a test subject (e.g., an undiagnosedindividual) can be compared to the model of all the data, regardless ofthe level of variance and uniqueness of the activation state data. Theactivation state data may be compared using a correlation metric, afitting metric or any other value that can be used to representsimilarity to a range of values.

In some embodiments, the activation state data for a test subject (e.g.,an undiagnosed individual) is plotted alongside data that represent therange of activation levels observed in normal cells. The range ofactivation levels observed in normal cells may be displayed or plottedas a scatterplot, a line graph with error bars, a histogram, a bar andwhisker plot, a radar plot, and/or a bar graph for example. In someembodiments, activation state data for a test subject (e.g., anundiagnosed individual) is depicted in a heat map alongside data thatrepresent the activation levels observed in normal cells. See FIGS. 9Band 9C for an example of a heat map. In some embodiments, correlationsbetween nodes in different cell populations are illustrated using acircular plot, where nodes with a positive correlation (e.g., >0.5) areconnected by a line of one color and nodes with a negative correlation(e.g., ≦−0.5) are connected by a line of a different color.

In some embodiments, the relative distribution of the cells intodiscrete cell populations is used to determine the similarity betweenthe test subject (e.g., an undiagnosed individual) and normal cells. Inthese embodiments, the normal samples are analyzed to determine therelative percentages of the different cell populations. From these data,a range of percentages of cell populations can be derived. Using therange of observed values and the variance in the observed values, ametric that indicates similarity and a confidence interval may beproduced. In one embodiment, the similarity value represents the overallsimilarity of the distribution over the different cell populations tothe distribution observed in the normal samples and the confidenceinterval represents the probability of observing such similarity basedon the distributions observed in the normal samples. This similarityvalue may be calculated independently from the similarity valuecalculated based on the activation levels or may be calculated incombination with the similarity value calculated based on the activationlevels. This similarity value can indicate how similar the distributionof cell-types in a test sample are to the range of percentages ofcell-types in normal samples.

In one embodiment, activation state data associated with the normalsamples may be combined with data derived from samples that are known tobe associated with disease states in order to generate a traditionalbinary or multi-class classifier. This classifier may be usedexperimentally to identify activation levels that distinguish thedisease state from normal cells. This classifier may also be used toperform diagnoses of specific diseases. In a specific embodiment,activation state data from samples from normal individuals may begenerated, analyzed and sold to various medical test developers for thispurpose.

In some embodiments, methods described herein, comparison of data fromnormal cells to data from cells from a test subject (e.g., anundiagnosed subject), can be used for drug screening, diagnosis,prognosis, or prediction of disease treatment. In some embodiments, themethods described herein can be used to measure signaling pathwayactivity in single cells, identify signaling pathway disruptions indiseased cells, including rare cell populations, identify response andresistant biological profiles that guide the selection of therapeuticregimens, monitor the effects of therapeutic treatments on signaling indiseased cells, or monitor the effects of treatment over time. In someembodiments, the methods provided herein can enable biology-drivenpatient management and drug development, improve patient outcome, reduceinefficient uses of resources, and improve speed of drug developmentcycles.

Modulators

In some embodiments, the methods and compositions utilize a modulator. Amodulator can be an activator, a therapeutic compound, an inhibitor or acompound capable of impacting a cellular pathway. Modulators can alsotake the form of environmental cues and inputs. Modulators can beuncharacterized or characterized as known compounds. A modulator can bea biological specimen or sample of a cellular or physiologicalenvironment from an individual, which may be a heterogeneous samplewithout complete chemical or biological characterization. Collection ofthe modulator specimen may occur directly from the individual, or beobtained indirectly. An illustrative example would be to remove acellular sample from the individual, and then culture that sample toobtain modulators.

Modulation can be performed in a variety of environments. In someembodiments, cells are exposed to a modulator immediately aftercollection. In some embodiments where there is a mixed population ofcells, purification of cells is performed after modulation. In someembodiments, whole blood is collected to which a modulator is added. Insome embodiments, cells are modulated after processing for single cellsor purified fractions of single cells. As an illustrative example, wholeblood can be collected and processed for an enriched fraction oflymphocytes that is then exposed to a modulator. Modulation can includeexposing cells to more than one modulator. For instance, in someembodiments, cells are exposed to at least 2, 3, 4, 5, 6, 7, 8, 9, or 10modulators. In some embodiments, cells are exposed to 1-10, 1-7, 1-5,2-10, 2-7, or 2-5 modulators. See U.S. Patent Application 61/048,657which is incorporated by reference.

In some embodiments, cells are cultured post collection in a suitablemedia before exposure to a modulator. In some embodiments, the media isa growth media. In some embodiments, the growth media is a complex mediathat may include serum. In some embodiments, the growth media comprisesserum. In some embodiments, the serum is selected from the groupconsisting of fetal bovine serum, bovine serum, human serum, porcineserum, horse serum, and goat serum. In some embodiments, the serum levelranges from about 0.0001% to 30%, about 0.001% to 30%, about 0.01% to30%, about 0.1% to 30% or 1% to 30%. In some embodiments, the growthmedia is a chemically defined minimal media and is without serum. Insome embodiments, cells are cultured in a differentiating media.

Modulators include chemical and biological entities, and physical orenvironmental stimuli. Modulators can act extracellularly orintracellularly. Chemical and biological modulators include growthfactors, cytokines, drugs, immune modulators, ions, neurotransmitters,adhesion molecules, hormones, small molecules, inorganic compounds,polynucleotides, antibodies, natural compounds, lectins, lactones,chemotherapeutic agents, biological response modifiers, carbohydrate,proteases and free radicals. Modulators include complex and undefinedbiologic compositions that may comprise cellular or botanical extracts,cellular or glandular secretions, physiologic fluids such as serum,amniotic fluid, or venom. Physical and environmental stimuli includeelectromagnetic, ultraviolet, infrared or particulate radiation, redoxpotential and pH, the presence or absence of nutrients, changes intemperature, changes in oxygen partial pressure, changes in ionconcentrations and the application of oxidative stress. Modulators canbe endogenous or exogenous and may produce different effects dependingon the concentration and duration of exposure to the single cells orwhether they are used in combination or sequentially with othermodulators. Modulators can act directly on the activatable elements orindirectly through the interaction with one or more intermediarybiomolecule. Indirect modulation includes alterations of gene expressionwherein the expressed gene product is the activatable element or is amodulator of the activatable element. A modulator can include, e.g., apsychological stressor.

In some embodiments the modulator is selected from the group consistingof growth factors, cytokines, adhesion molecules, drugs, hormones, smallmolecules, polynucleotides, antibodies, natural compounds, lactones,chemotherapeutic agents, immune modulators, carbohydrates, proteases,ions, reactive oxygen species, peptides, and protein fragments, eitheralone or in the context of cells, cells themselves, viruses, andbiological and non-biological complexes (e.g., beads, plates, viralenvelopes, antigen presentation molecules such as majorhistocompatibility complex). In some embodiments, the modulator is aphysical stimuli such as heat, cold, UV radiation, and radiation.Examples of modulators, include but are not limited to SDF-1α, IFN-α,IFN-γ, IL-10, IL-6, IL-27, G-CSF, FLT-3L, IGF-1, M-CSF, SCF, PMA,Thapsigargin, H₂O₂, etoposide, AraC, daunorubicin, staurosporine,benzyloxycarbonyl-Val-Ala-Asp (OMe) fluoromethylketone (ZVAD),lenalidomide, EPO, azacitadine, decitabine, IL-3, IL-4, GM-CSF, EPO,LPS, TNF-α, and CD40L. In some embodiments, the modulator is achemokine, e.g., CCL1, CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8,CCL9/CCL10, CCL11, CCL12, CCL13, CCL14, CCL15, CCL16, CCL17, CCL18,CCL19, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28,CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10,CXCL11, CXC12, CXCL13, CXCL14, CXCL15, CXCL16, CXCL17, XCL1, XCL2, orCX3CL1. In some embodiments, the modulator is an interleukin, e.g., IL-1alpha, IL-1 beta, IL-2, IL-3, IL-4, IL-5, IL-6 (BSF-2), IL-7, IL-8,IL-9, IL-10, IL-11, IL-12, IL-13, IL-14, IL-15, IL-16, IL-17, IL-18,IL-19, IL-20, IL-21, IL-22, IL-23, IL-24, IL-25, IL-26, IL-27, IL-28,IL-29, IL-30, IL-31, IL-32, IL-33 or IL-35.

In some embodiments, the modulator is an activator. In some embodimentsthe modulator is an inhibitor. In some embodiments, cells are exposed toone or more modulators. In some embodiments, cells are exposed to atleast 2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators. In some embodiments,cells are exposed to at least two modulators, wherein one modulator isan activator and one modulator is an inhibitor. In some embodiments,cells are exposed to at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators,where at least one of the modulators is an inhibitor. In someembodiments cells are exposed to 1-10, 1-7, 1-5, 2-10, 2-7, or 2-5modulators, where at least one of the modulators is an inhibitor.

In some embodiments, the inhibitor is an inhibitor of a cellular factoror a plurality of factors that participates in a cellular pathway (e.g.,signaling cascade) in the cell. In some embodiments, the inhibitor is aphosphatase inhibitor. Examples of phosphatase inhibitors include, butare not limited to H₂O₂, siRNA, miRNA, Cantharidin,(−)-p-Bromotetramisole, Microcystin LR, Sodium Orthovanadate, SodiumPervanadate, Vanadyl sulfate, Sodiumoxodiperoxo(1,10-phenanthroline)vanadate, bis(maltolato)oxovanadium(IV),Sodium Molybdate, Sodium Perm olybdate, Sodium Tartrate, Imidazole,Sodium Fluoride, β-Glycerophosphate, Sodium Pyrophosphate Decahydrate,Calyculin A, Discodermia calyx, bpV(phen), mpV(pic), DMHV, Cypermethrin,Dephostatin, Okadaic Acid, NIPP-1,N-(9,10-Dioxo-9,10-dihydro-phenanthren-2-yl)-2,2-dimethyl-propionamide,α-Bromo-4-hydroxyacetophenone, 4-Hydroxyphenacyl Br,α-Bromo-4-methoxyacetophenone, 4-Methoxyphenacyl Br,α-Bromo-4-(carboxymethoxy)acetophenone, 4-(Carboxymethoxy)phenacyl Br,and bis(4-Trifluoromethylsulfonamidophenyl)-1,4-diisopropylbenzene,phenylarsine oxide, Pyrrolidine Dithiocarbamate, and Aluminium fluoride.In some embodiments, the phosphatase inhibitor is H₂O₂.

In some embodiments, the activation level of an activatable element in acell is determined by contacting the cell with at least 2, 3, 4, 5, 6,7, 8, 9, or 10 modulators. In some embodiments, the activation level ofan activatable element in a cell is determined by contacting the cellwith at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators where at leastone of the modulators is an inhibitor. In some embodiments theactivation level of an activatable element in a cell is determined bycontacting the cell with 1-10, 1-7, 1-5, 2-10, 2-7, or 2-5 modulators.In some embodiments, the activation level of an activatable element in acell is determined by contacting the cell with an inhibitor and amodulator, where the modulator can be an inhibitor or an activator. Insome embodiments, the activation level of an activatable element in acell is determined by contacting the cell with an inhibitor and anactivator. In some embodiments, the activation level of an activatableelement in a cell is determined by contacting the cell with two or moremodulators.

In some embodiments, the physiological status of a population of cellsis determined by measuring the activation level of an activatableelement when the population of cells is exposed to one or moremodulators. The population of cells can be divided into a plurality ofsamples, and the physiological status of the population can bedetermined by measuring the activation level of at least one activatableelement in the samples after the samples have been exposed to one ormore modulators. In some embodiments, the physiological status ofdifferent populations of cells is determined by measuring the activationlevel of an activatable element in each population of cells when each ofthe populations of cells is exposed to a modulator. The differentpopulations of cells can be exposed to the same or different modulators.In some embodiments, the modulators include H₂O₂, PMA, SDF1α, CD40L,IGF-1, IL-7, IL-6, IL-10, IL-27, IL-4, IL-2, IL-3, thapsigardin and/or acombination thereof. For instance, a population of cells can be exposedto one or more, all, or a combination of the following combination ofmodulators: H₂O₂ PMA; SDF1α; CD40L; IGF-1; IL-7; IL-6; IL-10; IL-27;IL-4; IL-2; IL-3; thapsigardin. In some embodiments, the physiologicalstatus of different populations of cells is used to determine the statusof an individual as described herein. In some embodiments, the modulatoris a chemokine, e.g., CCL1, CCL2, CCL3, CCL4, CCL5, CCL6, CCL7, CCL8,CCL9/CCL10, CCL11, CCL12, CCL13, CCL14, CCL15, CCL16, CCL17, CCL18,CCL19, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28,CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10,CXCL11, CXC12, CXCL13, CXCL14, CXCL15, CXCL16, CXCL17, XCL1, XCL2, orCX3CL1. In some embodiments, the modulator is an interleukin, e.g., IL-1alpha, IL-1 beta, IL-2, IL-3, IL-4, IL-5, IL-6 (BSF-2), IL-7, IL-8,IL-9, IL-10, IL-11, IL-12, IL-13, IL-14, IL-16, IL-17, IL-18, IL-19,IL-20, IL-21, IL-22, IL-23, IL-24, IL-25, IL-26, IL-27, IL-28, IL-29,IL-30, IL-31, IL-32, IL-33 or IL-35.

In some embodiments, a modulator can be a FLT3 inhibitor (e.g., AC220,e.g., at 100 nM; Tandutinib [T] e.g., at 0.5 uM), a DNA damaging agent(e.g., AraC, e.g., at 0.5 μg/ml, 2 um)), A DNMT inhibitor (e.g.,zazcitidine, e.g., at 2.5 μM or Decitabine, e.g., at 0.625 μM)), a PARPinhibitor (e.g., AZD2281, e.g., at 5 μM), a PI3K and mTor dual inhibitor(e.g., BEZ235, e.g., at 50 nM), a proteosome inhibitor (e.g., bortezomibat 10 nM or 50 nM), a PI3 Kdelta inhibitor (e.g., CAL-101, e.g., at 0.5μM), a MEK inhibitor (e.g., AZD6244, e.g., at 1 μM), a DNA synthesisinhibitor (e.g., clofarabine, e.g., at 0.25 μM), a JAK inhibitor (e.g.,CP690550(CP)), e.g., at 1 μM; CYT387 e.g., at 1 μM; INCB018424 at 1μM)), a topoisomerase inhibitor (e.g., etoposide, e.g., at 15 μg/ml), amTor inhibitor (e.g., Everolimus (RAD0001) e.g., at 10 nM), a PI3Kinhibitor (e.g., GDC-0941 [G] e.g., at 1 μM), a BCR-ABL, cKit, or PDGR-Rinhibitor (e.g., Imatinib e.g., at 1 μM), an HSP90 inhibitor (e.g.,NVP-AUY922 e.g., at 50 nM), a VEGFR, PDGFR, RAF, FLT3, or cKIT inhibitor(e.g., Sorafenib, e.g., at 5 μM), a PDGF-R, VEGF-R, cKIT, FLT3, RET, orCSF-1R inhibitor (e.g., Sunitinib, e.g., at 50 nM), an alkylating agent(e.g., Temozolomide, e.g., at 2 μg/ml (10.3 μM), or an HDAC inhibitor(e.g., Vorinostat (SAHA, Zolinza, e.g., at 2.5 uμM). See Table 1 foradditional information on modulators and exemplary concentrations of themodulators.

TABLE 1 Exemplary drugs and concentrations of drugs. Mechanism Drug andof concentration Action Details AC220 100 nM FLT3 AC220 can be used totreat Acute Myeloid Leukemia (AML), a inhibitor common type of bloodcancer in adults. AC220 can target the kinase FLT3, which is mutated andconstitutively activated in 25-40 percent of AML patients, causing poorprognosis and decreased response to existing treatments includingchemotherapy and stem cell treatments. AC220 can be orally bioavailableand can induce tumor regression in a xenograft model at low doses. Ref:http://bloodjournal.hematologylibrary.org/content/114/14/2984.full.AC220 can be well tolerated and escalated to 450 mg daily on anintermittent dosing regimen, and PK has been evaluated up to 300 mg.AC220 half-life can be 2.5 days, exhibiting minimal peak and troughvariation of plasma levels. AC220 plasma exposure in AML patients can besustained between dose intervals and can continue to increase in adose-proportional manner from 12 mg to 300 mg daily, with steady-stateplasma concentrations achieving greater than 1,500 nM at 300 mg.Administering a 100 nM concentration of AC220 can block ~80-90% of theFLT3 induced pAKT signal. AraC 0.5 μg/ml DNA AraC (cytarbine) can beused to treat certain types of leukemia and (2 μM) damaging can preventthe spread of leukemia to the meninges (three thin agent layers oftissue that cover and protect the brain and spinal cord). Cytarabine canacts through direct DNA damage and incorporation into DNA. Cytarabinecan be cytotoxic to a wide variety of proliferating mammalian cells inculture. It can exhibit cell phase specificity, primarily killing cellsundergoing DNA synthesis (S- phase) and under certain conditions canblock the progression of cells from the G1 phase to the S-phase.Cytarabine can inhibit DNA polymerase. A limited, but significant,incorporation of cytarabine into both DNA and RNA has also beenreported. Cmax = 10 μM after 100 ng/m², proportionally higher up to 3g/m² (2 H inf.) Azacitidine 2.5 μM DNMT Cells in the presence ofazacitidine incorporate it into DNA during Inhibitor replication and RNAduring transcription. The incorporation of azacitidine into DNA or RNAinhibits methyltransferase thereby causing demethylation in thatsequence, affecting the way that cell regulation proteins are able tobind to the DNA/RNA substrate. Inhibition of DNA methylation occursthrough the formation of stable complexes between the molecule and withDNA methyltransferases, thereby saturating cell methylation machinery.In vivo: Cmax = 1.42-4.72 μM. AZD2281 PARP AZD2281 (Olaparib) can beused to treat breast, ovarian, and 5 μM inhibitor prostate cancerscaused by mutations in the BRCA1 and BRCA2 genes. AZD2281 can be a PARPinhibitor. MTD (maximum tolerated dose) can be 400 mg bd, continuously.Cmax (maximum plasma concention) can be ~6 ug/ml (13.8uM) at MTD. PD(pharmacodynamic) effects can be seen at doses >60 mg. BEZ235 PI3K andBEZ235 or NVP-BEZ235 can be an imidazoquinoline derivative 50 nM mTordual and PI3K inhibitor. BEZ235 can inhibit PI3K and mTOR kinaseinhibitor activity by binding to the ATP-binding cleft of these enzymes.Ref. Maira, SM., et al Mol Cancer Ther, 2008, 7(7). Preclinical datashow that BEZ235 has strong anti-proliferative activity against tumourxenografts that have abnormal PI3K signalling, including loss of PTENfunction or gain-of-function PI3K mutations. Pharmcologically activeexposure levels can reach doses of 400-1100 mg/ day (decreased pS6, CT,PET; ASCO 2010). pAKT and pS6 IC50 on H460 cell line can be 10 nM and 50nM respectively. Bortezomib Proteosome Bortezomib can be a drug used totreat multiple myeloma. It can be 10 nM and inhibitor used to treatmantle cell lymphoma in patients who have already 50 nM* received atleast one other type of treatment. Bortezomib can block severalmolecular pathways in a cell and can cause cancer cells to die. It canbe a type of proteasome inhibitor and a type of dipeptidyl boronic acid.Also called PS-341 and velcade. 10 nM blocks proteome activity [BLOOD,16 DECEMBER 2010 VOLUME 116, NUMBER 25]. Effect of Bort on theprolifeation of AML cell lines: IC90 ~10-50 nM. [haematologica. 2008Jan; 93(1): 57-66]. In vivo: tandard dose of 1.3 mg/m2 twice weekly for2 wks (day 1-4-8-11), with 1 wk rest, for up to 8 cycles. Ave Cmax = 130ng/ml (338.33 nM). Prescribing info says Cmax is 112 ng/ml (291 nM) witha T_(1/2) of 76 to 108 hrs. CAL-101 PI3Kdelta CAL-101 can be a potentand selective inhibitor of PI3K-δ iso form. 0.5 μM inhibitor NodalityIC₅₀ (anti-IgM_pAKT induced PBMC ~10 nM. 40 nM blocked ~90%. Ref:Herman, Sarah EM et al. Blood. Jun. 3, 2010 prepub online.(http://bloodjournal.hematologylibrary.org/content/early/2010/06/03/blood-2010-02-271171.full.pdf+html). Increases in Cmax and AUC can be lessthan dose proportional, revealing minimal gains in plasma exposure atdose levels >150 mg BID. The mean volume of distribution can be moderateat 57.7 L. The t_(1/2) can be ~8 hours across all dose levels and therecan be no plasma accumulation over 7 or 28 days. The collective datasupport BID dosing at ≧150 mg; dose levels in this range maintainsteady-state trough plasma concentrations that are >10-fold above theEC50 for the in vitro whole-blood assay.” 620 nM may be the steady stateconcentration. AZD6244 1uM MEK AZD6244 (ARRY-142886) can be a potent,selective, and ATP inhibitor uncompetitive inhibitor of MEK½ kinases.Activating mutations in the BRAF gene, e.g., V600E, are associated withpoorer outcomes in patients with papillary thyroid cancer. MAPK kinase(MEK), immediately downstream of BRAF, is a promising target forras-raf-MEK-ERK pathway inhibition. In addition to thyroid cancer,BRAF-activating mutations can be prevalent in melanoma (−59%),colorectal cancer (5-22%), serous ovarian cancer (−30%), and severalother tumor types. Davies H et al. Nature. 2002 Jun 27; 417(6892):949-54 At twice daily dosing (75 mg), Cmax can be 1439 ng/ml (3.2 μM) at1 hr post dose. PD effects of ~80% pERK inhibition can be seen at ~1000ng/ml plasma conc. in blood lymphocytes used as a surrogate readout(Clin Cancer Res; 16(5) Mar. 1, 2010). At 1 μM in vitro, 85-95% of PMAinduced pERK can be inhibited (IC₅₀ ~100 nM) in lymphocytes from PBMCs.Clofarabine DNA Clofarabine (Clolar, Genzyme) has been studied in thetreatment of 0.25 μM synthesis various types of leukemia and is FDAapproved for the treatment of inhibitor childhood acute lymphoblasticleukemia. It is structurally related to fludarabine and cladribine,sharing some characteristics and avoiding others. Clofarabine can exertits antineoplastic activity through several mechanisms. The activemetabolite of clofarabine can be its triphosphate form. This moleculecan compete with deoxyadenosine triphosphate for the ribonucleotidereductase and DNA polymerase, which can lead to decreased DNA synthesisand repair, inhibit DNA strand elongation and cell replication.Pretreatment with clofarabine before cytarabine administration can leadto increases in intracellular concentrations of cytarabine triphosphate,the active form of cytarabine. The standard dose of clofarabine can be52 mg/m2 for pediatrics and 40 mg/m2 in adults which leads to anaccumulation of plasma clofarabine of 0.5 to 3 μM. (Clin Cancer Res2003; 9: 6335-6342) CP690550 [CP] JAKs CP690550 can be a JAK3 inhibitor.The somatic activating janus 1 μM kinase 2 mutation (JAK2)V617F can bedetectable in most patients with polycythemia vera (PV). Enzymaticassays indicate that both JAK1 and JAK2 are 100- and 20-fold lesssensitive to inhibition by CP-690,550, respectively, when compared withJAK3. JAK2V617F-bearing cells were almost 10-fold more sensitive toCP-690,550 compared with JAK2WT cells, with IC₅₀s of 0.25 μM and 2.11μM, respectively. In vivo: 30 mg BID. Cmax = 364.39 ng/ml (1.16 uM), T½2.6 hrs, (Br J Clin Pharmacol/69:2/143-151/ 143). GM-CSF_pSTAT5inhibition can be ~300 nM IC₅₀ (JAK2 driven) and ~130 nM for G-CSF (JAK3driven). CYT387 JAK CYT387 can be a JAK inhibitor. Reported activities:(biochemical) 1 μM inhibitor JAK2 (18 nM), JAK1(11 nM), JAK3 (155).Ba/F3-wt (+IL-3, proliferation) JAK2 wt 1424 nM. PBMCs (monos)/GM-CSF/pSTAT5 can have 1109 nM IC₅₀ with IC90 ~333 nM. pAKT inhibition(same cells, same stim) can have 129 nM IC₅₀ with ~1000 nM IC90.http://www.nature.com/leu/journal/v23/n8/pdf/leu200950a.pdf DecitabineDNMT Decitabine (Dacogen) is a drug that can be used to treat 0.625 μMinhibitor myelodysplastic syndromes. It can be a type of antimetabolite.Decitabine is indicated for treatment of patients with myelodysplasticsyndrome (MDS). Decitabine can exert its antineoplastic effectsfollowing its conversion to decitabine triphosphate, where the drugdirectly incorporates into DNA and inhibits DNA methyltransferase, theenzyme that is responsible for methylating newly synthesized DNA inmammalian cells. This can result in hypomethylation of DNA and cellulardifferentiation or apoptosis. Decitabine inhibits DNA methylation invitro, which can be achieved at concentrations that do not cause majorsuppression of DNA synthesis. Decitabine-induced hypomethylation inneoplastic cells can restore normal function to genes that play a rolein the control of cellular differentiation and proliferation. In rapidlydividing cells, the cytotoxicity of decitabine can also be attributed tothe formation of covalent adducts between DNA methyltransferase anddecitabine that has been incorporated into DNA. Non- proliferating cellscan be relatively insensitive to decitabine. Decitabine can be cellcycle specific and can act peripherally in the S phase of the cellcycle. In AML cell lines (KG-1, THP-1), decitabine can inhibit DNMT1 at0.1 μM Cmax (IV 15 mg/m2 IV over 3 hrs, every 8 hrs, for 3 days) can be0.3-1.6 μM (Hollenbach PW et al. PLoS ONE 5(2): e9001). Decitabine canbe used at 0.625 μM in vitro 24-48 hrs. Etoposide topoisomeraseEtoposide (Toposar, Vepesid) can be used to treat testicular and 15μg/ml inhibitor small cell lung cancers. Etoposide can block certainenzymes used needed for cell division and DNA repair, and it can killcancer cells. Etoposide is a podophyllotoxin derivative and can inhibittopoisomerase. Two different dose-dependent responses can be observedwith etoposide. At high concentrations (10 μg/mL or more), lysis ofcells entering mitosis can be observed. At low concentrations (0.3 to 10μg/mL), cells can be inhibited from entering prophase. Etoposide caninduce DNA strand breaks by an interaction with DNA-topoisomerase II orthe formation of free radicals. In adults with normal renal and hepaticfunction, an 80 mg/m2 IV dose given over 1 hour averaged an etoposideplasma Cmax of 14.9 mcg/ml. Following 500 mg/h IV infusions of 400, 500,or 600 mg/m2, etoposide plasma peak concentrations of 26 to 53, 27 to73, and 42 to 114 mcg/ml, respectively, can be attained. With continuousIV infusion of 100 mg/m2 daily for 72 hours, plasma drug concentrationsof 2 to 5 mcg/ml can be reached 2 to 3 hours after the start of infusionand can be maintained until the end of infusion. In children 3 months to16 years of age with normal renal and hepatic function, IV infusions of200 to 250 mg/m2 given over 0.5 to 2.25 hours can result in peak serumetoposide concentrations ranging from 17 to 88 mcg/ml. Everolimus mTorEverolimus (also known as RAD001) can bind and create a complex (RAD001)inhibitor with FKBP12 and can interact with mTor to inhibit downstream10 nM signaling events. In vivo dosing can be either 10 mg/d or 50 mg/wk[O'Donnell et al, JCO, 26, (10) Apr. 1, 2008]. At 10 mg/d the Cmax canbe 61 ng/ml (63 nM) and the trough can be 17 ng/ml (17.7 nM). At 50mg/wk the trough can be 1 ng/ml (~1 nM). [J Clin Oncol 26: 1603-1610.2008]. A 10 nM dose in vitro can inhibit p-S6 completely as well asinhibit proliferation of mantle cell line (Jeko) [TE Witzig et al,Leukemia (2010), 1-7]. GDC-0941 [G] PI3K GDC-0941 can be a PI3Kinhibitor. GDC-0941 against p110a can 1 μM have an IC₅₀ = 0.003 μM,U87MG; IC₅₀ = 0.95 μM, A2780 I IC₅₀ = 0.14 μM, and in vitro metabolicstability in mouse and human can be 91.96%. The inhibitions of U87MG,PC3, MDA-MB-361 cancer cell proliferation can be (IC50) 0.95, 0.28, and0.72. GDC-0941 can display dose-proportional increases in mean Cmax andAUCinf. Decreases in pS6 staining of >50% can occur in paired tumorbiopsies in addition to decreases of >90% in pAKT levels assayed in PRPfrom patientss treated at 80 mg and higher. Signs of biologic activitycan be observed in 3 patientss (ovarian cancer, triple negative breastcancer, and ocular melanoma) treated at ≧100 mg GDC-0941 with reductions(≧30% in mean SUVmax) in tumor FDG avidity observed on PET scan and an~80% decrease in CA-125 in an ovarian cancer patient, who remainedon-study for ~5 months. Conclusions: GDC-0941 can be generally welltolerated at 15 to 130 mg QD. Decreases in pAKT levels in PRP anddecreases in pS6 staining in paired tumor biopsies are consistent withdownstream modulation of the PI3K pathway. Imatinib BCR- Imatinib(Gleevec or STI571) can be used to treat different types of 1 μM ABL,leukemia and other cancers of the blood, gastrointestinal stromal cKit,tumors, skin tumors called dermatofibrosarcoma protuberans, and a PDGF-Rrare condition called systemic mastocytosis. Imatinib mesylate can blockthe protein made by the bcr/abl oncogene. It is a type of tyrosinekinase inhibitor. The plasma trough level of imatinib at steady statecan be slightly higher in females than males (1078 [1] 515 ng/mL vs 921531 ng/mL, respectively). (BLOOD, 15 APRIL 2008_VOLUME 111, NUMBER 8).Assume trough of 1000 ng/ml = 2 μM. INCB018424 JAK INCB018424 phosphatecan be a potent inhibitor of JAK enzymes 1 μM inhibitor with selectivityfor JAK1&2, and can be used for the treatment of myelofibrosis (MF). Invivo, 25 mg bid and 100 mg qd can be the maximum tolerated doses inhealthy volunteers. INCB018424 dosing: 25 mg bid and 100 mg qd can bethe maximum tolerated doses in healthy volunteers. At 100 mg 24 h: Cmax4780 nM; Tmax = 1.5 hrs; T½ = 2.8 hrs. The plasma conc. was ~1000 nM at6 hrs post-dose. (Shi et al. J Clin Pharmacol, published online 21 Jan2011.) 1000 nM can completely inhibit GM-CSF_pSTAT5 (IC₅₀ = 215 nM).NVP-AUY922 HSP90 HSP90 can be a ubiquitously expressed molecularchaperone that 50 nM can play a role in the post-translationalconformational maturation and activation of a large number of clientproteins that have been implicated in oncogenesis. Inhibition of theATPase activity at the N-terminus of HSP90 is being exploited by allinhibitors that have entered the clinic so far. In competitivefluorescence polarization assays, NVP-AUY922 inhibited HSP90α and HSP90βwith similar IC₅₀ (median inhibition concentration) values of 13 and 21nM respectively. In a representative panel of human tumor cell lines(including prostate, breast, ovarian, colon, lung, melanoma, andglioblastoma), NVP-AUY922 can inhibit cell proliferation with lownanomolar potency; GI50 (the concentration that inhibits cell growth by50%) values can be in the range of 2.3 to 50 nM. Sorafenib 5 μM VEGFR,Sorafenib can be used to treat advanced kidney cancer and a type ofPDGFR, liver cancer that cannot be removed by surgery. Sorafenibtosylate RAF, can stop cells from dividing and can prevent the growth ofnew FLT3, blood vessels that tumors need to grow. It can inhibit kinasesand act cKIT as an antiangiogenesis agent. Sorafenib can also be calledBAY 43- 9006, or Nexavar. Steady state C trough level can be 3 mg/ml at400 mg BID which equals 6.4 μM. Sunitinib 50 nM PDGF-R, Sunitinib can beused to treat gastrointestinal stromal tumors (GIST) VEGF-R, that havenot responded to treatment with imatinib mesylate cKit, (Gleevec).Sunitinib can also used to treat advanced kidney cancer. FLT3, It can bea type of tyrosine kinase inhibitor, a type of vascular RET, endothelialgrowth factor (VEGF) receptor inhibitor, and a type of CSF-1Rangiogenesis inhibitor. It can be called SU011248, SU11248, sunitinibmalate, and Sutent. T max can be between 6 and 12 h. With repeat dailydosing, sunitinib can accumulate 3- to 4-fold, and the primary activemetabolite can accumulate 7- to 10-fold. Steady- state concentrations ofthe primary drug and primary metabolite can be achieved within 10 to 14days. The combined plasma levels of sunitinib plus active metabolite canrange from 62.9 to 101 ng/mL (125.5 nM to 253.4 nM). Tandutinib [T] FLT3Tandutinib (CT53518 and MLN518) can stop cancer cell growth by 0.5 μMinhibitor blocking certain enzymes and can also prevent the growth ofnew blood vessels that tumors need to grow. Tandutinib can inhibittyrosine kinases and can act as an antiangiogenesis agent. Tandutinibcan be given orally in doses ranging from 50 mg to 700 mg twice dailyThe principal dose-limiting toxicity (DLT) of tandutinib can bereversible generalized muscular weakness, fatigue, or both, occurring atdoses of 525 mg and 700 mg twice daily. Tandutinib's pharmacokineticscan be characterized by slow elimination, with achievement ofsteady-state plasma concentrations requiring greater than 1 week ofdosing. Tandutinib can inhibit phosphorylation of FLT3 in circulatingleukemic blasts. Eight patients had FLT3-ITD mutations; 5 of these wereevaluable for assessment of tandutinib's antileukemic effect. Two of the5 patients, treated at 525 mg and 700 mg twice daily, showed evidence ofantileukemic activity, with decreases in both peripheral and bone marrowblasts. (Blood. 2006 December 1; 108(12): 3674-3681). At this dose amean plasma concentration can be ~300 ng/ml (533 nM). Temozolomidealkylating Temozolomide (TMZ) is an imidazotetrazine derivative of the 2μg/ml (10.3 μM) agent alkylating agent dacarbazine. It can undergo rapidchemical conversion in the systemic circulation at physiological pH tothe active compound, MTIC (monomethyl triazeno imidazole carboxamide).Temozolomide can exhibit schedule-dependent antineoplastic activity byinterfering with DNA replication. Temozolomide can demonstrate activityagainst recurrent glioma. In a recent randomized trial, concomitant andadjuvant temozolomide chemotherapy with radiation significantly canimprove progression free survival and overall survival in glioblastomamultiforme patients. Adult MTD = 200 ng/m2/day (Seiter K et al. J ClinOnco 20: 3249-3253, 2002). 200 ng/m2/day = 9.3 ug/ml(47.9 μM). The T½ is100 min. Two μg/ml (10.3 μM) is well below the Cmax. Vorinostat HDACVorinostat (SAHA) is a synthetic hydroxamic acid derivative that (SAHA,inhibitor can have antineoplastic activity. Vorinostat, a secondgeneration Zolinza) 2.5 μM polar-planar compound, can bind to thecatalytic domain of the histone deacetylases (HDACs). This can allow thehydroxamic moiety to chelate zinc ion located in the catalytic pocketsof HDAC, thereby inhibiting deacetylation and leading to an accumulationof both hyperacetylated histones and transcription factors.Hyperacetylation of histone proteins can result in the upregulation ofthe cyclin-dependent kinase p21, followed by G1 arrest. Hyperacetylationof non-histone proteins such as tumor suppressor p53, alpha tubulin, andheat-shock protein 90 can produce additional anti-proliferative effects.This agent can also induce apoptosis and sensitize tumor cells to celldeath processes. SAHA can be used at 2.5 μM (0.66 μg/ml). Cmax canbe1.81 +/− .70 μM [1.11-2.51 μM]. A concentration of 2.5 μM is withinthe Cmax and is also near the reported ED50 reported for AML cells lines(Hollenbach PW et al. PLoS ONE 5(2): e9001)

Activatable Elements

The methods and compositions described herein may be employed to examineand profile the status of any activatable element in a cellular pathway,or collections of such activatable elements. Single or multiple distinctpathways may be profiled (sequentially or simultaneously), or subsets ofactivatable elements within a single pathway or across multiple pathwaysmay be examined (again, sequentially or simultaneously).

Typically, a cell possesses a plurality of a particular protein or otherconstituent with a particular activatable element and this plurality ofproteins or constituents usually has some proteins or constituents whoseindividual activatable element is in the on state and other proteins orconstituents whose individual activatable element is in the off state.Since the activation state of each activatable element can be measuredthrough the use of a binding element that recognizes a specificactivation state, only those activatable elements in the specificactivation state recognized by the binding element, representing somefraction of the total number of activatable elements, can be bound bythe binding element to generate a measurable signal. The measurablesignal corresponding to the summation of individual activatable elementsof a particular type that are activated in a single cell can be the“activation level” for that activatable element in that cell. Theactivation state of an individual activatable element can be representedas continuous numeric values representing a quantity of the activatableelement or can be discretized into categorical variables. For instance,the activation state may be discretized into a binary value indicatingthat the activatable element is either in the on or off state. As anillustrative example, and without intending to be limited to any theory,an individual phosphorylatable site on a protein can be phosphorylatedand then be in the “on” state or it can not be phosphorylated and hence,it will be in the “off’state. See Blume-Jensen and Hunter, Nature, vol411, 17 May 2001, p 355-365. The terms “on” and “off,” when applied toan activatable element that is a part of a cellular constituent, can beused here to describe the state of the activatable element (e.g.,phosphorylated is “on” and non-phosphorylated is “off’), and not theoverall state of the cellular constituent of which it is a part.

Activation levels for a particular activatable element may vary amongindividual cells so that when a plurality of cells is analyzed, theactivation levels follow a distribution. The distribution may be anormal distribution, also known as a Gaussian distribution, or it may beof another type. Different populations of cells may have differentdistributions of activation levels that can then serve to distinguishbetween the populations.

In some embodiments, the basis for determining the activation levels ofone or more activatable elements in cells may use the distribution ofactivation levels for one or more specific activatable elements whichwill differ among different phenotypes. A certain activation level, ormore typically a range of activation levels for one or more activatableelements seen in a cell or a population of cells, is indicative thatthat cell or population of cells belongs to a distinctive phenotype.Other measurements, such as cellular levels (e.g., expression levels) ofbiomolecules that may not contain activatable elements, may also be usedto determine the physiological status of a cell in addition toactivation levels of activatable elements; it will be appreciated thatthese levels also will follow a distribution, similar to activatableelements. Thus, the activation level or levels of one or moreactivatable elements, optionally in conjunction with levels of one ormore levels of biomolecules that may not contain activatable elements,of one or more cells in a population of cells may be used to determinethe physiological status of the cell population.

In some embodiments, the basis for determining the physiological statusof a population of cells may use the position of a cell in a contour ordensity plot of the distribution of the activation levels. The contouror density plot represents the number of cells that share acharacteristic such as the activation level of activatable proteins inresponse to a modulator. For example, when referring to activationlevels of activatable elements in response to one or more modulators,normal individuals and patients with a condition might show populationswith increased activation levels in response to the one or moremodulators. However, the number of cells that have a specific activationlevel (e.g., a specific amount of an activatable element) might bedifferent between cells from normal individuals and cells from patientswith a condition. Thus, the physiological status of a cell can bedetermined according to its location within a given region in thecontour or density plot.

In a specific embodiment, methods may be used to represent thedistribution of the activation levels as a one-dimensional vector ofvalues. For additional information, see e.g., PCT Publication No.WO/2007/117423.

In another specific embodiments, methods may be used to model the datawithin the homogeneous population of cells. These methods mayincorporate state transition modeling as outlines. Bayesian network,belief network or directed acyclic graphical model can be aprobabilistic graphical model that can represent a set of randomvariables and their conditional dependencies via a directed acyclicgraph (DAG). For example, a Bayesian network can represent theprobabilistic relationships between diseases and symptoms. Givensymptoms, the network can be used to compute the probabilities of thepresence of various diseases. For additional information, see e.g., U.S.Patent Application No. 20070009923.

In addition to activation levels of intracellular activatable elements,expression levels of intracellular or extracellular biomolecules, e.g.,proteins, may be used alone or in combination with activation states ofactivatable elements to determine the physiological status of apopulation of cells. Further, additional cellular elements, e.g.,biomolecules or molecular complexes such as RNA, DNA, carbohydrates,metabolites, and the like, may be used in conjunction with activatablestates, expression levels or any combination of activatable states andexpression levels in the determination of the physiological status of apopulation of cells encompassed here.

In some embodiments, other characteristics that affect the status of acellular constituent may also be used to determine the physiologicalstatus of a cell. Examples include the translocation of biomolecules orchanges in their turnover rates and the formation and disassociation ofcomplexes of a biomolecule. Such complexes can include multi-proteincomplexes, multi-lipid complexes, homo- or hetero-dimers or oligomers,and combinations thereof. Other characteristics include proteolyticcleavage, e.g., from exposure of a cell to an extracellular protease orfrom the intracellular proteolytic cleavage of a biomolecule.

Additional elements may also be used to determine the physiologicalstatus of a cell, such as the expression level of extracellular orintracellular markers, nuclear antigens, enzymatic activity, proteinexpression and localization, cell cycle analysis, chromosomal analysis,teleomere length analysis, telomerase activity, cell volume, andmorphological characteristics like granularity and size of nucleus orother distinguishing characteristics. For example, myeloid lineage cellscan be further subdivided based on the expression of cell surfacemarkers such as CD14, CD15, or CD33, CD34 and CD45.

Alternatively, different homogeneous populations of cells can beaggregated based upon shared characteristics that may include inclusionin one or more additional cell populations or the presence ofextracellular or intracellular markers, similar gene expression profile,nuclear antigens, enzymatic activity, protein expression andlocalization, cell cycle analysis, chromosomal analysis, cell volume,teleomere length analysis, telomerase activity, and morphologicalcharacteristics like granularity and size of nucleus or otherdistinguishing characteristics.

In some embodiments, the physiological status of one or more cells isdetermined by examining and profiling the activation level of one ormore activatable elements in a cellular pathway. In some embodiments,the activation levels of one or more activatable elements of a cell froma first population of cells and the activation levels of one or moreactivatable elements of a cell from a second population of cells arecorrelated with a condition. In some embodiments, the first and secondhomogeneous populations of cells are hematopoietic cell populations. Insome embodiments, the activation levels of one or more activatableelements of a cell from a first population of hematopoietic cells andthe activation levels of one or more activatable elements of cell from asecond population of hematopoietic cells are correlated with aneoplastic, autoimmune or hematopoietic condition as described herein.Examples of different populations of hematopoietic cells include, butare not limited to, pluripotent hematopoietic stem cells, B-lymphocytelineage progenitor or derived cells, T-lymphocyte lineage progenitor orderived cells, NK cell lineage progenitor or derived cells, granulocytelineage progenitor or derived cells, monocyte lineage progenitor orderived cells, megakaryocyte lineage progenitor or derived cells anderythroid lineage progenitor or derived cells.

In some embodiments, the activation level of one or more activatableelements in single cells in the sample is determined. Cellularconstituents that may include activatable elements include withoutlimitation proteins, carbohydrates, lipids, nucleic acids andmetabolites. The activatable element may be a portion of the cellularconstituent, for example, an amino acid residue in a protein that mayundergo phosphorylation, or it may be the cellular constituent itself,for example, a protein that is activated by translocation, change inconformation (due to, e.g., change in pH or ion concentration), byproteolytic cleavage, and the like. Upon activation, a change occurs tothe activatable element, such as covalent modification of theactivatable element (e.g., binding of a molecule or group to theactivatable element, such as phosphorylation) or a conformationalchange. Such changes generally contribute to changes in particularbiological, biochemical, or physical properties of the cellularconstituent that contains the activatable element. The state of thecellular constituent that contains the activatable element is determinedto some degree, though not necessarily completely, by the state of aparticular activatable element of the cellular constituent. For example,a protein may have multiple activatable elements, and the particularactivation states of these elements may overall determine the activationstate of the protein; the state of a single activatable element is notnecessarily determinative. Additional factors, such as the binding ofother proteins, pH, ion concentration, interaction with other cellularconstituents, and the like, can also affect the state of the cellularconstituent.

In some embodiments, the activation levels of a plurality ofintracellular activatable elements in single cells are determined. Theterm “plurality” as used herein refers to two or more. In someembodiments, at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10intracellular activatable elements are determined. In some embodiments,about 1-10, 1-7, 1-5, 2-10, 2-7, or 2-5 intracellular activatableelements are determined.

Activation states of activatable elements may result from chemicaladditions or modifications of biomolecules and include biochemicalprocesses such as glycosylation, phosphorylation, acetylation,methylation, biotinylation, glutamylation, glycylation, hydroxylation,isomerization, prenylation, myristoylation, lipoylation,phosphopantetheinylation, sulfation, ISGylation, nitrosylation,palmitoylation, SUMOylation, ubiquitination, neddylation,citrullination, amidation, and disulfide bond formation, disulfide bondreduction. Other possible chemical additions or modifications ofbiomolecules include the formation of protein carbonyls, directmodifications of protein side chains, such as o-tyrosine, chloro-,nitrotyrosine, and dityrosine, and protein adducts derived fromreactions with carbohydrate and lipid derivatives. Other modificationsmay be non-covalent, such as binding of a ligand or binding of anallosteric modulator.

In some embodiments, the activatable element is a protein. Examples ofproteins that may include activatable elements include, but are notlimited to kinases, phosphatases, lipid signaling molecules,adaptor/scaffold proteins, cytokines, cytokine regulators,ubiquitination enzymes, adhesion molecules, cytoskeletal/contractileproteins, heterotrimeric G proteins, small molecular weight GTPases,guanine nucleotide exchange factors, GTPase activating proteins,caspases, proteins involved in apoptosis, cell cycle regulators,molecular chaperones, metabolic enzymes, vesicular transport proteins,hydroxylases, isomerases, deacetylases, methylases, demethylases, tumorsuppressor genes, proteases, ion channels, molecular transporters,transcription factors/DNA binding factors, regulators of transcription,and regulators of translation. Examples of activatable elements,activation states and methods of determining the activation level ofactivatable elements are described in US Publication Number 20060073474entitled “Methods and compositions for detecting the activation state ofmultiple proteins in single cells” and US Publication Number 20050112700entitled “Methods and compositions for risk stratification” the contentof which are incorporate here by reference. See also U.S. Ser. Nos.61/048,886, 61/048,920 and Shulz et al, Current Protocols in Immunology2007, 7:8.17.1-20.

In some embodiments, the protein that may be activated is selected fromthe group consisting of HER receptors, PDGF receptors, FLT3 receptor,Kit receptor, FGF receptors, Eph receptors, Trk receptors, IGFreceptors, Insulin receptor, Met receptor, Ret, VEGF receptors,erythropoetin receptor, thromobopoetin receptor, CD114, CD116, TIE1,TIE2, FAK, Jak1, Jak2, Jak3, Tyk2, Src, Lyn, Fyn, Lck, Fgr, Yes, Csk,Abl, Btk, ZAP70, Syk, IRAKs, cRaf, ARaf, BRAF, Mos, Lim kinase, ILK,Tpl, ALK, TGFβ receptors, BMP receptors, MEKKs, ASK, MLKs, DLK, PAKs,Mek 1, Mek 2, MKK3/6, MKK4/7, ASK1, Cot, NIK, Bub, Myt 1, Weel, Caseinkinases, PDK1, SGK1, SGK2, SGK3, Akt1, Akt2, Akt3, p90Rsks, p70S6Kinase,Prks, PKCs, PKAs, ROCK 1, ROCK 2, Auroras, CaMKs, MNKs, AMPKs, MELK,MARKs, Chk1, Chk2, LKB-1, MAPKAPKs, Pim1, Pim2, Pim3, IKKs, Cdks, Jnks,Erks, IKKs, GSK3a, GSK3β, Cdks, CLKs, PKR, PI3-Kinase class 1, class 2,class 3, mTor, SAPK/JNK1, 2, 3, p38s, PKR, DNA-PK, ATM, ATR, Receptorprotein tyrosine phosphatases (RPTPs), LAR phosphatase, CD45, Nonreceptor tyrosine phosphatases (NPRTPs), SHPs, MAP kinase phosphatases(MKPs), Dual Specificity phosphatases (DUSPs), CDC25 phosphatases, Lowmolecular weight tyrosine phosphatase, Eyes absent (EYA) tyrosinephosphatases, Slingshot phosphatases (SSH), serine phosphatases, PP2A,PP2B, PP2C, PP1, PP5, inositol phosphatases, PTEN, SHIPs, myotubularins,phosphoinositide kinases, phopsholipases, prostaglandin synthases,5-lipoxygenase, sphingosine kinases, sphingomyelinases, adaptor/scaffoldproteins, Shc, Grb2, BLNK, LAT, B cell adaptor for PI3-kinase (BCAP),SLAP, Dok, KSR, MyD88, Crk, CrkL, GAD, Nck, Grb2 associated binder(GAB), Fas associated death domain (FADD), TRADD, TRAF2, RIP, T-Cellleukemia family, IL-2, IL-4, IL-8, IL-6, interferon γ, interferon α,suppressors of cytokine signaling (SOCs), Cbl, SCF ubiquitination ligasecomplex, APC/C, adhesion molecules, integrins, Immunoglobulin-likeadhesion molecules, selectins, cadherins, catenins, focal adhesionkinase, p130CAS, fodrin, actin, paxillin, myosin, myosin bindingproteins, tubulin, eg5/KSP, CENPs, β-adrenergic receptors, muscarinicreceptors, adenylyl cyclase receptors, small molecular weight GTPases,H-Ras, K-Ras, N-Ras, Ran, Rac, Rho, Cdc42, Arfs, RABs, RHEB, Vav, Tiam,Sos, Dbl, PRK, TSC1, 2, Ras-GAP, Arf-GAPs, Rho-GAPs, caspases, Caspase2, Caspase 3, Caspase 6, Caspase 7, Caspase 8, Caspase 9, Bcl-2, Mcl-1,Bcl-XL, Bcl-w, Bcl-B, Al, Bax, Bak, Bok, Bik, Bad, Bid, Bim, Bmf, Hrk,Noxa, Puma, IAPs, XIAP, Smac, Cdk4, Cdk 6, Cdk 2, Cdk1, Cdk 7, Cyclin D,Cyclin E, Cyclin A, Cyclin B, Rb, p16, p14Arf, p27KIP, p21CIP, molecularchaperones, Hsp90s, Hsp70, Hsp27, metabolic enzymes, Acetyl-CoAaCarboxylase, ATP citrate lyase, nitric oxide synthase, caveolins,endosomal sorting complex required for transport (ESCRT) proteins,vesicular protein sorting (Vsps), hydroxylases, prolyl-hydroxylasesPHD-1, 2 and 3, asparagine hydroxylase FM transferases, Pin1 prolylisomerase, topoisomerases, deacetylases, Histone deacetylases, sirtuins,histone acetylases, CBP/P300 family, MYST family, ATF2, DNA methyltransferases, Histone H3K4 demethylases, H3K27, JHDM2A, UTX, VHL, WT-1,p53, Hdm, PTEN, ubiquitin proteases, urokinase-type plasminogenactivator (uPA) and uPA receptor (uPAR) system, cathepsins,metalloproteinases, esterases, hydrolases, separase, potassium channels,sodium channels, multi-drug resistance proteins, P-Gycoprotein,nucleoside transporters, Ets, Elk, SMADs, Rel-A (p65-NFKB), CREB, NFAT,ATF-2, AFT, Myc, Fos, Spl, Egr-1, T-bet, β-catenin, HIFs, FOXOs, E2Fs,SRFs, TCFs, Egr-1, β-catenin, FOXO STAT1, STAT 3, STAT 4, STAT 5, STAT6, p53, Ets-1, Ets-2, SPDEF, GABPα, Tel, Tel2, WT-1, HMGA, pS6, 4EPB-1,eIF4E-binding protein, RNA polymerase, initiation factors, elongationfactors.

In some embodiments, the methods described herein are employed todetermine the activation level of an activatable element, e.g., in acellular pathway. Methods and compositions are provided for thedetermination of the physiological status of a cell according to theactivation level of an activatable element in a cellular pathway.Methods and compositions are provided for the determination of thephysiological status of a cell in a first cell population and a cell ina second cell population according to the activation level of anactivatable element in a cellular pathway in each cell. The cells can bea hematopoietic cell and examples are provided herein.

In some embodiments, the determination of the physiological status ofcells in different populations according to activation level of anactivatable element, e.g., in a cellular pathway comprises classifyingat least one of the cells as a cell that is correlated with a clinicaloutcome. Examples of clinical outcomes, staging, as well as patientresponses are provided herein.

Signaling Pathways

In some embodiments, the methods described herein are employed todetermine the activation level of an activatable element in a signalingpathway. In some embodiments, the physiological status of a cell isdetermined, as described herein, according to the activation level ofone or more activatable elements in one or more signaling pathways.Signaling pathways and their members have been extensively described.See (Hunter T. Cell Jan. 7, 2000; 100(1): 13-27; Weinberg, 2007; andBlume-Jensen and Hunter, Nature, vol 411, 17 May 2001, p 355-365 citedabove). Exemplary signaling pathways include the following pathways andtheir members: the JAK-STAT pathway including JAKs, STATs 2, 3 4 and 5,the FLT3L signaling pathway, the MAP kinase pathway including Ras, Raf,MEK, ERK and Elk; the PI3K/Akt pathway including PI-3-kinase, PDK1, Aktand Bad; the NF-κB pathway including IKKs, IkB and NF-κB and the Wntpathway including frizzled receptors, beta-catenin, APC and otherco-factors and TCF (see Cell Signaling Technology, Inc. 2002 Catalogpages 231-279 and Hunter T., supra.). In some embodiments, thecorrelated activatable elements being assayed (or the signaling proteinsbeing examined) are members of the MAP kinase, Akt, NFkB, WNT, STATand/or PKC signaling pathways.

In some embodiments, methods are employed to determine the activationlevel of a signaling protein in a signaling pathway known in the artincluding those described herein. Exemplary types of signaling proteinsinclude, but are not limited to, kinases, kinase substrates (i.e.,phosphorylated substrates), phosphatases, phosphatase substrates,binding proteins (such as 14-3-3), receptor ligands and receptors (cellsurface receptor tyrosine kinases and nuclear receptors)). Kinases andprotein binding domains, for example, have been well described (see,e.g., Cell Signaling Technology, Inc., 2002 Catalogue “The Human ProteinKinases” and “Protein Interaction Domains” pgs. 254-279).

Exemplary signaling proteins include, but are not limited to, kinases,HER receptors, PDGF receptors, Kit receptor, FGF receptors, Ephreceptors, Trk receptors, IGF receptors, Insulin receptor, Met receptor,Ret, VEGF receptors, TIE1, TIE2, FAK, Jak1, Jak2, Jak3, Tyk2, Src, Lyn,Fyn, Lck, Fgr, Yes, Csk, Abl, Btk, ZAP70, Syk, IRAKs, cRaf, ARaf, BRAF,Mos, Lim kinase, ILK, Tpl, ALK, TGFβ receptors, BMP receptors, MEKKs,ASK, MLKs, DLK, PAKs, Mek 1, Mek 2, MKK3/6, MKK4/7, ASK1, Cot, NIK, Bub,Myt 1, Weel, Casein kinases, PDK1, SGK1, SGK2, SGK3, Akt1, Akt2, Akt3,p90Rsks, p70S6Kinase, Prks, PKCs, PKAs, ROCK 1, ROCK 2, Auroras, CaMKs,MNKs, AMPKs, MELK, MARKs, Chk1, Chk2, LKB-1, MAPKAPKs, Pim1, Pim2, Pim3,IKKs, Cdks, Jnks, Erks (e.g., Erk1, Erk2), IKKs, GSK3a, GSK3β, Cdks,CLKs, PKR, PI3-Kinase class 1, class 2, class 3, mTor, SAPK/JNK1, 2, 3,p38s, PKR, DNA-PK, ATM, ATR, phosphatases, Receptor protein tyrosinephosphatases (RPTPs), LAR phosphatase, CD45, Non receptor tyrosinephosphatases (NPRTPs), SHPs, MAP kinase phosphatases (MKPs), DualSpecificity phosphatases (DUSPs), CDC25 phosphatases, low molecularweight tyrosine phosphatase, Eyes absent (EYA) tyrosine phosphatases,Slingshot phosphatases (SSH), serine phosphatases, PP2A, PP2B, PP2C,PP1, PP5, inositol phosphatases, PTEN, SHIPs, myotubularins, lipidsignaling, phosphoinositide kinases, phopsholipases, prostaglandinsynthases, 5-lipoxygenase, sphingosine kinases, sphingomyelinases,adaptor/scaffold proteins, Shc, Grb2, BLNK, LAT, B cell adaptor forPI3-kinase (BCAP), SLAP, Dok, KSR, MyD88, Crk, CrkL, GAD, Nck, Grb2associated binder (GAB), Fas associated death domain (FADD), TRADD,TRAF2, RIP, T-Cell leukemia family, cytokines, IL-2, IL-4, IL-8, IL-6,interferon γ, interferon α, cytokine regulators, suppressors of cytokinesignaling (SOCs), ubiquitination enzymes, Cbl, SCF ubiquitination ligasecomplex, APC/C, adhesion molecules, integrins, Immunoglobulin-likeadhesion molecules, selectins, cadherins, catenins, focal adhesionkinase, p130CAS, cytoskeletal/contractile proteins, fodrin, actin,paxillin, myosin, myosin binding proteins, tubulin, eg5/KSP, CENPs,heterotrimeric G proteins, β-adrenergic receptors, muscarinic receptors,adenylyl cyclase receptors, small molecular weight GTPases, H-Ras,K-Ras, N-Ras, Ran, Rac, Rho, Cdc42, Arfs, RABs, RHEB, guanine nucleotideexchange factors, Vav, Tiam, Sos, Dbl, PRK, TSC1, 2, GTPase activatingproteins, Ras-GAP, Arf-GAPs, Rho-GAPs, caspases, Caspase 2, Caspase 3,Caspase 6, Caspase 7, Caspase 8, Caspase 9, proteins involved inapoptosis, Bcl-2, Mcl-1, Bcl-XL, Bcl-w, Bcl-B, Al, Bax, Bak, Bok, Bik,Bad, Bid, Bim, Bmf, Hrk, Noxa, Puma, IAPs, XIAP, Smac, cell cycleregulators, Cdk4, Cdk 6, Cdk 2, Cdk1, Cdk 7, Cyclin D, Cyclin E, CyclinA, Cyclin B, Rb, p16, p14Arf, p27KIP, p21CIP, molecular chaperones,Hsp90s, Hsp70, Hsp27, metabolic enzymes, Acetyl-CoAa Carboxylase, ATPcitrate lyase, nitric oxide synthase, vesicular transport proteins,caveolins, endosomal sorting complex required for transport (ESCRT)proteins, vesicular protein sorting (Vsps), hydroxylases,prolyl-hydroxylases PHD-1, 2 and 3, asparagine hydroxylase FIHtransferases, isomerases, Pin1 prolyl isomerase, topoisomerases,deacetylases, Histone deacetylases, sirtuins, acetylases, histoneacetylases, CBP/P300 family, MYST family, ATF2, methylases, DNA methyltransferases, demethylases, Histone H3K4 demethylases, H3K27, JHDM2A,UTX, tumor suppressor genes, VHL, WT-1, p53, Hdm, PTEN, proteases,ubiquitin proteases, urokinase-type plasminogen activator (uPA) and uPAreceptor (uPAR) system, cathepsins, metalloproteinases, esterases,hydrolases, separase, ion channels, potassium channels, sodium channels,molecular transporters, multi-drug resistance proteins, P-Gycoprotein,nucleoside transporters, transcription factors/DNA binding proteins,Ets, Elk, SMADs, Rel-A (p65-NFKB), CREB, NFAT, ATF-2, AFT, Myc, Fos,Spl, Egr-1, T-bet, β-catenin, HIFs, FOXOs, E2Fs, SRFs, TCFs, Egr-1,β-catenin, FOXO STAT1, STAT 3, STAT 4, STAT 5, STAT 6, p53, WT-1, HMGA,regulators of translation, pS6, 4EPB-1, eIF4E-binding protein,regulators of transcription, RNA polymerase, initiation factors, andelongation factors.

In some embodiments the protein is selected from the group consisting ofPI3-Kinase (p85, p110a, p110b, p110d), Jak1, Jak2, SOCs, Rac, Rho,Cdc42, Ras-GAP, Vav, Tiam, Sos, Dbl, Nck, Gab, PRK, SHP1, and SHP2,SHIM, SHIP2, sSHIP, PTEN, Shc, Grb2, PDK1, SGK, Akt1, Akt2, Akt3, TSC1,2, Rheb, mTor, 4EBP-1, p70S6Kinase, S6, LKB-1, AMPK, PFK, Acetyl-CoAaCarboxylase, DokS, Rafs, Mos, Tpl2, MEK1/2, MLK3, TAK, DLK, MKK3/6,MEKK1, 4, MLK3, ASK1, MKK4/7, SAPK/JNK1, 2, 3, p38s, Erk1/2, Syk, Btk,BLNK, LAT, ZAP70, Lck, Cbl, SLP-76, PLCyi, PLCy 2, STAT1, STAT 3, STAT4, STAT 5, STAT 6, FAK, p130CAS, PAKs, LIMK1/2, Hsp90, Hsp70, Hsp27,SMADs, Rel-A (p65-NFKB), CREB, Histone H₂B, HATs, HDACs, PKR, Rb, CyclinD, Cyclin E, Cyclin A, Cyclin B, P16, p14Arf, p27KIP, p21C1P, Cdk4,Cdk6, Cdk7, Cdk1, Cdk2, Cdk9, Cdc25, A/B/C, Abl, E2F, FADD, TRADD,TRAF2, RIP, Myd88, BAD, Bcl-2, Mcl-1, Bcl-XL, Caspase 2, Caspase 3,Caspase 6, Caspase 7, Caspase 8, Caspase 9, IAPs, Smac, Fodrin, Actin,Src, Lyn, Fyn, Lck, NIK, IκB, p65(Rel A), IKKα, PKA, PKCα, PKC β, PKCθ,PKCδ, CAMK, Elk, AFT, Myc, Egr-1, NFAT, ATF-2, Mdm2, p53, DNA-PK, Chk1,Chk2, ATM, ATR, βcatenin, CrkL, GSK3α, GSK3β, and FOXO.

In some embodiments, the methods described herein are employed todetermine the activation level of an activatable element in a signalingpathway. See U.S. Ser. Nos. 61/048,886 and 61/048,920 which areincorporated by reference. Methods and compositions are provided for thedetermination of a physiological status of a cell according to thestatus of an activatable element in a signaling pathway. Methods andcompositions are provided for the determination of a physiologicalstatus of cells in different populations of cells according to thestatus of an activatable element in a signaling pathway. The cells canbe hematopoietic cells. Examples of hematopoietic cells are providedherein.

In some embodiments, the determination of a physiological status ofcells in different populations of cells according to the activationlevel of an activatable element in a signaling pathway comprisesclassifying the cell populations as cells that are correlated with aclinical outcome. Examples of clinical outcome, staging, patientresponses and classifications are provided herein.

Binding Element

In some embodiments, the activation level of an activatable element isdetermined. In one embodiment, the determination is made by contacting acell from a cell population with a binding element that is specific foran activation state of the activatable element. The term “bindingelement” can include any molecule, e.g., peptide, nucleic acid, smallorganic molecule which is capable of detecting an activation state of anactivatable element over another activation state of the activatableelement. Binding elements and labels for binding elements are shown inU.S. Ser. No. 61/048,886; 61/048,920 and 61/048,657.

In some embodiments, the binding element is a peptide, polypeptide,oligopeptide or a protein. The peptide, polypeptide, oligopeptide orprotein may be made up of naturally occurring amino acids and peptidebonds, or synthetic peptidomimetic structures. Thus “amino acid”, or“peptide residue”, as used herein can include both naturally occurringand synthetic amino acids. For example, homo-phenylalanine, citrullineand noreleucine are considered amino acids. The side chains may be ineither the (R) or the (S) configuration. In some embodiments, the aminoacids are in the (S) or L-configuration. If non-naturally occurring sidechains are used, non-amino acid substituents may be used, for example toprevent or retard in vivo degradation. Proteins including non-naturallyoccurring amino acids may be synthesized or in some cases, maderecombinantly; see van Hest et al., FEBS Lett 428:(1-2) 68-70 May 22,1998 and Tang et al., Abstr. Pap Am. Chem. 5218: U138 Part 2 Aug. 22,1999, both of which are expressly incorporated by reference herein.

Methods described herein may be used to detect any particularactivatable element in a sample that is antigenically detectable andantigenically distinguishable from another activatable element which ispresent in the sample. For example, the activation state-specificantibodies can be used in the present methods to identify distinctsignaling cascades of a subset or subpopulation of complex cellpopulations; and/or the ordering of protein activation (e.g., kinaseactivation) in potential signaling hierarchies. Hence, in someembodiments the expression and phosphorylation of one or morepolypeptides are detected and quantified using methods described herein.In some embodiments, the expression and phosphorylation of one or morepolypeptides that are cellular components of a cellular pathway aredetected and quantified using methods described herein. As used herein,the term “activation state-specific antibody” or “activation stateantibody” or grammatical equivalents thereof, can refer to an antibodythat specifically binds to a corresponding and specific antigen. Thecorresponding and specific antigen can be a specific form of anactivatable element. The binding of the activation state-specificantibody can be indicative of a specific activation state of a specificactivatable element.

In some embodiments, the binding element is an antibody. In someembodiments, the binding element is an activation state-specificantibody.

The term “antibody” can include full length antibodies and antibodyfragments, and may refer to a natural antibody from any organism, anengineered antibody, or an antibody generated recombinantly forexperimental, therapeutic, or other purposes as further defined below.Examples of antibody fragments, as are known in the art, such as Fab,Fab′, F(ab′)2, Fv, scFv, or other antigen-binding subsequences ofantibodies, either produced by the modification of whole antibodies orthose synthesized de novo using recombinant DNA technologies. The term“antibody” comprises monoclonal and polyclonal antibodies. Antibodiescan be antagonists, agonists, neutralizing, inhibitory, or stimulatory.They can be humanized, glycosylated, bound to solid supports, or possesother variations. See U.S. Ser. Nos. 61/048,886, 61/048,920, and61/048,657 for more information about antibodies as binding elements.

Activation state specific antibodies can be used to detect kinaseactivity. Additional means for determining kinase activation areprovided herein. For example, substrates that are specificallyrecognized by protein kinases and phosphorylated thereby are known.Antibodies that specifically bind to such phosphorylated substrates butdo not bind to such non-phosphorylated substrates (phospho-substrateantibodies) may be used to determine the presence of activated kinase ina sample.

The antigenicity of an activated isoform of an activatable element canbe distinguishable from the antigenicity of non-activated isoform of anactivatable element or from the antigenicity of an isoform of adifferent activation state. In some embodiments, an activated isoform ofan element possesses an epitope that is absent in a non-activatedisoform of an element, or vice versa. In some embodiments, thisdifference is due to covalent addition of a moiety to an element, suchas a phosphate moiety, or due to a structural change in an element, asthrough protein cleavage, or due to an otherwise induced conformationalchange in an element which causes the element to present the samesequence in an antigenically distinguishable way. In some embodiments,such a conformational change causes an activated isoform of an elementto present at least one epitope that is not present in a non-activatedisoform, or to not present at least one epitope that is presented by anon-activated isoform of the element. In some embodiments, the epitopesfor the distinguishing antibodies are centered around the active site ofthe element, although as is known in the art, conformational changes inone area of an element may cause alterations in different areas of theelement as well.

Many antibodies, many of which are commercially available (for example,see Cell Signaling Technology, www.cellsignal.com or Becton Dickinson,www.bd.com) have been produced which specifically bind to thephosphorylated isoform of a protein but do not specifically bind to anon-phosphorylated isoform of a protein. Many such antibodies have beenproduced for the study of signal transducing proteins which arereversibly phosphorylated. Particularly, many such antibodies have beenproduced which specifically bind to phosphorylated, activated isoformsof protein. Examples of proteins that can be analyzed with the methodsdescribed herein include, but are not limited to, kinases, HERreceptors, PDGF receptors, FLT3 receptor, Kit receptor, FGF receptors,Eph receptors, Trk receptors, IGF receptors, Insulin receptor, Metreceptor, Ret, VEGF receptors, TIE1, TIE2, erythropoetin receptor,thromobopoetin receptor, CD114, CD116, FAK, Jak1, Jak2, Jak3, Tyk2, Src,Lyn, Fyn, Lck, Fgr, Yes, Csk, Abl, Btk, ZAP70, Syk, IRAKs, cRaf, ARaf,BRAF, Mos, Lim kinase, ILK, Tpl, ALK, TGFβ receptors, BMP receptors,MEKKs, ASK, MLKs, DLK, PAKs, Mek 1, Mek 2, MKK3/6, MKK4/7, ASK1, Cot,NIK, Bub, Myt 1, Weel, Casein kinases, PDK1, SGK1, SGK2, SGK3, Akt1,Akt2, Akt3, p90Rsks, p70S6Kinase, Prks, PKCs, PKAs, ROCK 1, ROCK 2,Auroras, CaMKs, MNKs, AMPKs, MELK, MARKs, Chk1, Chk2, LKB-1, MAPKAPKs,Pim1, Pim2, Pim3, IKKs, Cdks, Jnks, Erks, GSK3a, GSK3β, Cdks, CLKs, PKR,PI3-Kinase class 1, class 2, class 3, mTor, SAPK/JNK1, 2, 3, p38s, PKR,DNA-PK, ATM, ATR, phosphatases, Receptor protein tyrosine phosphatases(RPTPs), LAR phosphatase, CD45, Non receptor tyrosine phosphatases(NPRTPs), SHPs, MAP kinase phosphatases (MKPs), Dual Specificityphosphatases (DUSPs), CDC25 phosphatases, Low molecular weight tyrosinephosphatase, Eyes absent (EYA) tyrosine phosphatases, Slingshotphosphatases (SSH), serine phosphatases, PP2A, PP2B, PP2C, PP1, PPS,inositol phosphatases, PTEN, SHIPs, myotubularins, lipid signaling,phosphoinositide kinases, phopsholipases, prostaglandin synthases,5-lipoxygenase, sphingosine kinases, sphingomyelinases, adaptor/scaffoldproteins, Shc, Grb2, BLNK, LAT, B cell adaptor for PI3-kinase (BCAP),SLAP, Dok, KSR, MyD88, Crk, CrkL, GAD, Nck, Grb2 associated binder(GAB), Fas associated death domain (FADD), TRADD, TRAF2, RIP, T-Cellleukemia family, cytokines, IL-2, IL-4, IL-8, IL-6, interferon γ,interferon α, cytokine regulators, suppressors of cytokine signaling(SOCs), ubiquitination enzymes, Cbl, SCF ubiquitination ligase complex,APC/C, adhesion molecules, integrins, Immunoglobulin-like adhesionmolecules, selectins, cadherins, catenins, focal adhesion kinase,p130CAS, cytoskeletal/contractile proteins, fodrin, actin, paxillin,myosin, myosin binding proteins, tubulin, eg5/KSP, CENPs, heterotrimericG proteins, β-adrenergic receptors, muscarinic receptors, adenylylcyclase receptors, small molecular weight GTPases, H-Ras, K-Ras, N-Ras,Ran, Rac, Rho, Cdc42, Arfs, RABs, RHEB, guanine nucleotide exchangefactors, Vav, Tiam, Sos, Dbl, PRK, TSC1, 2, GTPase activating proteins,Ras-GAP, Arf-GAPs, Rho-GAPs, caspases, Caspase 2, Caspase 3, Caspase 6,Caspase 7, Caspase 8, Caspase 9, proteins involved in apoptosis, Bcl-2,Mcl-1, Bcl-XL, Bcl-w, Bcl-B, Al, Bax, Bak, Bok, Bik, Bad, Bid, Bim, Bmf,Hrk, Noxa, Puma, IAPs, XIAP, Smac, cell cycle regulators, Cdk4, Cdk 6,Cdk 2, Cdk1, Cdk 7, Cyclin D, Cyclin E, Cyclin A, Cyclin B, Rb, p16,p14Arf, p27KIP, p21CIP, molecular chaperones, Hsp90s, Hsp70, Hsp27,metabolic enzymes, Acetyl-CoAa Carboxylase, ATP citrate lyase, nitricoxide synthase, vesicular transport proteins, caveolins, endosomalsorting complex required for transport (ESCRT) proteins, vesicularprotein sorting (Vsps), hydroxylases, prolyl-hydroxylases PHD-1, 2 and3, asparagine hydroxylase FIH transferases, isomerases, Pin1 prolylisomerase, topoisomerases, deacetylases, Histone deacetylases, sirtuins,acetylases, histone acetylases, CBP/P300 family, MYST family, ATF2,methylases, DNA methyl transferases, demethylases, Histone H3K4demethylases, H3K27, JHDM2A, UTX, tumor suppressor genes, VHL, WT-1,p53, Hdm, PTEN, proteases, ubiquitin proteases, urokinase-typeplasminogen activator (uPA) and uPA receptor (uPAR) system, cathepsins,metalloproteinases, esterases, hydrolases, separase, ion channels,potassium channels, sodium channels, molecular transporters, multi-drugresistance proteins, P-Gycoprotein, nucleoside transporters,transcription factors/DNA binding proteins, Ets family transcriptionfactors, Ets-1, Ets-2, Tel, Tel2, Elk, SMADs, Rel-A (p65-NFKB), CREB,NFAT, ATF-2, AFT, Myc, Fos, Spl, Egr-1, T-bet, β-catenin, HIFs, FOXOs,E2Fs, SRFs, TCFs, Egr-1, β-FOXO STAT1, STAT 3, STAT 4, STAT 5, STAT 6,p53, WT-1, HMGA, regulators of translation, pS6, 4EPB-1, eIF4E-bindingprotein, regulators of transcription, RNA polymerase, initiationfactors, elongation factors. In some embodiments, the protein is S6.

In some embodiments, an epitope-recognizing fragment of an activationstate antibody rather than the whole antibody is used. In someembodiments, the epitope-recognizing fragment is immobilized. In someembodiments, the antibody light chain that recognizes an epitope isused. A recombinant nucleic acid encoding a light chain gene productthat recognizes an epitope may be used to produce such an antibodyfragment by recombinant means well known in the art.

In some embodiments, aromatic amino acids of protein binding elementsmay be replaced with other molecules. See U.S. S. Nos. 61/048,886,61/048,920, and 61/048,657.

In some embodiments, the activation state-specific binding element is apeptide comprising a recognition structure that binds to a targetstructure on an activatable protein. A variety of recognition structuresare well known in the art and can be made using methods known in theart, including by phage display libraries (see e.g., Gururaja et al.Chem. Biol. (2000) 7:515-27; Houimel et al., Eur. J. Immunol. (2001)31:3535-45; Cochran et al. J. Am. Chem. Soc. (2001) 123:625-32; Houimelet al. Int. J. Cancer (2001) 92:748-55, each incorporated herein byreference). Further, fluorophores can be attached to such antibodies foruse in the methods described herein.

A variety of recognitions structures are known in the art (e.g., Cochranet al., J. Am. Chem. Soc. (2001) 123:625-32; Boer et al., Blood (2002)100:467-73, each expressly incorporated herein by reference) and can beproduced using methods known in the art (see e.g., Boer et al., Blood(2002) 100:467-73; Gualillo et al., Mol. Cell Endocrinol. (2002)190:83-9, each expressly incorporated herein by reference), includingfor example combinatorial chemistry methods for producing recognitionstructures such as polymers with affinity for a target structure on anactivatable protein (see e.g., Barn et al., J. Comb. Chem. (2001)3:534-41; Ju et al., Biotechnol. (1999) 64:232-9, each expresslyincorporated herein by reference). In another embodiment, the activationstate-specific antibody is a protein that only binds to an isoform of aspecific activatable protein that is phosphorylated and does not bind tothe isoform of this activatable protein when it is not phosphorylated ornonphosphorylated. In another embodiment the activation state-specificantibody is a protein that only binds to an isoform of an activatableprotein that is intracellular and not extracellular, or vice versa. Insome embodiments, the recognition structure is an anti-lamininsingle-chain antibody fragment (scFv) (see e.g., Sanz et al., GeneTherapy (2002) 9:1049-53; Tse et al., J. Mol. Biol. (2002) 317:85-94,each expressly incorporated herein by reference).

In some embodiments the binding element is a nucleic acid. The term“nucleic acid” include nucleic acid analogs, for example, phosphoramide(Beaucage et al., Tetrahedron 49(10):1925 (1993) and references therein;Letsinger, J. Org. Chem. 35:3800 (1970); Sprinzl et al., Eur. J.Biochem. 81:579 (1977); Letsinger et al., Nucl. Acids Res. 14:3487(1986); Sawai et al, Chem. Lett. 805 (1984), Letsinger et al., J. Am.Chem. Soc. 110:4470 (1988); and Pauwels et al., Chemica Scripta 26:14191986)), phosphorothioate (Mag et al., Nucleic Acids Res. 19:1437(1991); and U.S. Pat. No. 5,644,048), phosphorodithioate (Briu et al.,J. Am. Chem. Soc. 111:2321 (1989), O-methylphosphoroamidite linkages(see Eckstein, Oligonucleotides and Analogues: A Practical Approach,Oxford University Press), and peptide nucleic acid backbones andlinkages (see Egholm, J. Am. Chem. Soc. 114:1895 (1992); Meier et al.,Chem. Int. Ed. Engl. 31:1008 (1992); Nielsen, Nature, 365:566 (1993);Carlsson et al., Nature 380:207 (1996), all of which are incorporated byreference). Other analog nucleic acids include those with positivebackbones (Denpcy et al., Proc. Natl. Acad. Sci. USA 92:6097 (1995);non-ionic backbones (U.S. Pat. Nos. 5,386,023, 5,637,684, 5,602,240,5,216,141 and 4,469,863; Kiedrowshi et al., Angew. Chem. Intl. Ed.English 30:423 (1991); Letsinger et al., J. Am. Chem. Soc. 110:4470(1988); Letsinger et al., Nucleoside & Nucleotide 13:1597 (1994);Chapters 2 and 3, ASC Symposium Series 580, “Carbohydrate Modificationsin Antisense Research”, Ed. Y. S. Sanghui and P. Dan Cook; Mesmaeker etal., Bioorganic & Medicinal Chem. Lett. 4:395 (1994); Jeffs et al., J.Biomolecular NMR 34:17 (1994); Tetrahedron Lett. 37:743 (1996)) andnon-ribose backbones, including those described in U.S. Pat. Nos.5,235,033 and 5,034,506, and Chapters 6 and 7, ASC Symposium Series 580,“Carbohydrate Modifications in Antisense Research”, Ed. Y. S. Sanghuiand P. Dan Cook. Nucleic acids containing one or more carbocyclic sugarsare also included within the definition of nucleic acids (see Jenkins etal., Chem. Soc. Rev. (1995) pp 169-176). Several nucleic acid analogsare described in Rawls, C & E News Jun. 2, 1997 page 35. All of thesereferences are hereby expressly incorporated by reference. Thesemodifications of the ribose-phosphate backbone may be done to facilitatethe addition of additional moieties such as labels, or to increase thestability and half-life of such molecules in physiological environments.

In some embodiment the binding element is a small organic compound.Binding elements can be synthesized from a series of substrates that canbe chemically modified. “Chemically modified” herein includestraditional chemical reactions as well as enzymatic reactions. Thesesubstrates generally include, but are not limited to, alkyl groups(including alkanes, alkenes, alkynes and heteroalkyl), aryl groups(including arenes and heteroaryl), alcohols, ethers, amines, aldehydes,ketones, acids, esters, amides, cyclic compounds, heterocyclic compounds(including purines, pyrimidines, benzodiazepins, beta-lactams,tetracylines, cephalosporins, and carbohydrates), steroids (includingestrogens, androgens, cortisone, ecodysone, etc.), alkaloids (includingergots, vinca, curare, pyrollizdine, and mitomycines), organometalliccompounds, hetero-atom bearing compounds, amino acids, and nucleosides.Chemical (including enzymatic) reactions may be done on the moieties toform new substrates or binding elements that can then be used in themethods and compositions described herein.

In some embodiments the binding element is a carbohydrate. As usedherein the term carbohydrate can include any compound with the generalformula (CH₂0)_(n). Examples of carbohydrates are mono-, di-, tri- andoligosaccharides, as well polysaccharides such as glycogen, cellulose,and starches.

In some embodiments the binding element is a lipid. As used herein theterm lipid can include any water insoluble organic molecule that issoluble in nonpolar organic solvents. Examples of lipids are steroids,such as cholesterol, phospholipids such as sphingomeylin, and fattyacyls, glycerolipids, glycerophospholipids, sphingolipids,saccharolipids, and polyketides, including tri-, di- and monoglyceridesand phospholipids. The lipid can be a hydrophobic molecule oramphiphilic molecule.

Examples of activatable elements, activation states and methods ofdetermining the activation level of activatable elements are describedin US publication number 20060073474 entitled “Methods and compositionsfor detecting the activation state of multiple proteins in single cells”and US publication number 20050112700 entitled “Methods and compositionsfor risk stratification” the content of which are incorporate here byreference.

Labels

The methods and compositions provided herein provide binding elementscomprising a label or tag. By label is meant a molecule that can bedirectly (i.e., a primary label) or indirectly (i.e., a secondary label)detected; for example a label can be visualized and/or measured orotherwise identified so that its presence or absence can be known.Binding elements and labels for binding elements are shown in U.S. Ser.No. 61/048,886, 61/048,920, and 61/048,657.

A compound can be directly or indirectly conjugated to a label whichprovides a detectable signal, e.g., radioisotopes, fluorescers, enzymes,antibodies, particles such as magnetic particles, chemiluminescers,molecules that can be detected by mass spec, or specific bindingmolecules, etc. Specific binding molecules include pairs, such as biotinand streptavidin, digoxin and antidigoxin etc. Examples of labelsinclude, but are not limited to, optical fluorescent and chromogenicdyes including labels, label enzymes and radioisotopes. In someembodiments, these labels may be conjugated to the binding elements.

In some embodiments, one or more binding elements are uniquely labeled.Using the example of two activation state specific antibodies, by“uniquely labeled” is meant that a first activation state antibodyrecognizing a first activated element comprises a first label, andsecond activation state antibody recognizing a second activated elementcomprises a second label, wherein the first and second labels aredetectable and distinguishable, making the first antibody and the secondantibody uniquely labeled.

In general, labels can fall into four classes: a) isotopic labels, whichmay be radioactive or heavy isotopes; b) magnetic, electrical, thermallabels; c) colored, optical labels including luminescent, phosphorousand fluorescent dyes or moieties; and d) binding partners. Labels canalso include enzymes (horseradish peroxidase, etc.) and magneticparticles. In some embodiments, the detection label is a primary label.A primary label is one that can be directly detected, such as afluorophore.

Labels include optical labels such as fluorescent dyes or moieties.Fluorophores can be either “small molecule” fluors, or proteinaceousfluors (e.g., green fluorescent proteins and all variants thereof).

In some embodiments, activation state-specific antibodies are labeledwith quantum dots as disclosed by Chattopadhyay, P. K. et al. Quantumdot semiconductor nanocrystals for immunophenotyping by polychromaticflow cytometry. Nat. Med. 12, 972-977 (2006). Quantum dot labels arecommercially available through Invitrogen,http://probes.invitrogen.com/products/qdot/.

Quantum dot labeled antibodies can be used alone or they can be employedin conjunction with organic fluorochrome-conjugated antibodies toincrease the total number of labels available. As the number of labeledantibodies increase so does the ability for subtyping known cellpopulations. Additionally, activation state-specific antibodies can belabeled using chelated or caged lanthanides as disclosed by Erkki, J. etal. Lanthanide chelates as new fluorochrome labels for cytochemistry. J.Histochemistry Cytochemistry, 36:1449-1451, 1988, and U.S. Pat. No.7,018,850, entitled Salicylamide-Lanthanide Complexes for Use asLuminescent Markers. Other methods of detecting fluorescence may also beused, e.g., Quantum dot methods (see, e.g., Goldman et al., J. Am. Chem.Soc. (2002) 124:6378-82; Pathak et al. J. Am. Chem. Soc. (2001)123:4103-4; and Remade et al., Proc. Natl. Sci. USA (2000) 18:553-8,each expressly incorporated herein by reference) as well as confocalmicroscopy.

In some embodiments, the activatable elements are labeled with tagssuitable for Inductively Coupled Plasma Mass Spectrometer (ICP-MS) asdisclosed in Tanner et al. Spectrochimica Acta Part B: AtomicSpectroscopy, 2007 March; 62(3):188-195.

Detection systems based on FRET, discussed in detail below, may be used.FRET can be used in the methods described herein, for example, indetecting activation states that involve clustering or multimerizationwherein the proximity of two FRET labels is altered due to activation.In some embodiments, at least two fluorescent labels are used which aremembers of a fluorescence resonance energy transfer (FRET) pair.

The methods and compositions described herein may also make use of labelenzymes. By label enzyme is meant an enzyme that may be reacted in thepresence of a label enzyme substrate that produces a detectable product.Suitable label enzymes include but are not limited to, horseradishperoxidase, alkaline phosphatase and glucose oxidase. Methods for theuse of such substrates are well known in the art. The presence of thelabel enzyme is generally revealed through the enzyme's catalysis of areaction with a label enzyme substrate, producing an identifiableproduct. Such products may be opaque, such as the reaction ofhorseradish peroxidase with tetramethyl benzedine, and may have avariety of colors. Other label enzyme substrates, such as Luminol(available from Pierce Chemical Co.), have been developed that producefluorescent reaction products. Methods for identifying label enzymeswith label enzyme substrates are well known in the art and manycommercial kits are available. Examples and methods for the use ofvarious label enzymes are described in Savage et al., Previews 247:6-9(1998), Young, J. Virol. Methods 24:227-236 (1989), which are eachhereby incorporated by reference in their entirety.

By radioisotope is meant any radioactive molecule. Suitableradioisotopes include, but are not limited to ¹⁴C, ³H, ³²P, ³³P, ³⁵S,¹²⁵I and ¹³¹I. The use of radioisotopes as labels is well known in theart.

As mentioned, labels may be indirectly detected, that is, the tag is apartner of a binding pair. By “partner of a binding pair” is meant oneof a first and a second moiety, wherein the first and the second moietyhave a specific binding affinity for each other. Suitable binding pairsinclude, but are not limited to, antigens/antibodies (for example,digoxigenin/anti-digoxigenin, dinitrophenyl (DNP)/anti-DNP,dansyl-X-anti-dansyl, Fluorescein/anti-fluorescein, luciferyellow/anti-lucifer yellow, and rhodamine anti-rhodamine), biotin/avidin(or biotin/streptavidin) and calmodulin binding protein(CBP)/calmodulin. Other suitable binding pairs include polypeptides suchas the FLAG-peptide [Hopp et al., BioTechnology, 6:1204-1210 (1988)];the KT3 epitope peptide [Martin et al., Science, 255: 192-194 (1992)];tubulin epitope peptide [Skinner et al., J. Biol. Chem., 266:15163-15166(1991)]; and the T7 gene 10 protein peptide tag [Lutz-Freyermuth et al.,Proc. Natl. Acad. Sci. USA, 87:6393-6397 (1990)] and the antibodies eachthereto. Binding pair partners may be used in applications other thanfor labeling, as is described herein.

A partner of one binding pair may also be a partner of another bindingpair. For example, an antigen (first moiety) may bind to a firstantibody (second moiety) that may, in turn, be an antigen for a secondantibody (third moiety). It will be further appreciated that such acircumstance allows indirect binding of a first moiety and a thirdmoiety via an intermediary second moiety that is a binding pair partnerto each.

As will be appreciated by those in the art, a partner of a binding pairmay comprise a label, as described above. It will further be appreciatedthat this allows for a tag to be indirectly labeled upon the binding ofa binding partner comprising a label. Attaching a label to a tag that isa partner of a binding pair, as just described, is referred to herein as“indirect labeling”.

By “surface substrate binding molecule” or “attachment tag” andgrammatical equivalents thereof can be meant a molecule have bindingaffinity for a specific surface substrate, which substrate is generallya member of a binding pair applied, incorporated or otherwise attachedto a surface. Suitable surface substrate binding molecules and theirsurface substrates include, but are not limited to poly-histidine(poly-his) or poly-histidine-glycine (poly-his-gly) tags and Nickelsubstrate; the Glutathione-S Transferase tag and its antibody substrate(available from Pierce Chemical); the flu HA tag polypeptide and itsantibody 12CA5 substrate [Field et al., Mol. Cell. Biol., 8:2159-2165(1988)]; the c-myc tag and the 8F9, 3C7, 6E10, G4, B7 and 9E10 antibodysubstrates thereto [Evan et al., Molecular and Cellular Biology,5:3610-3616 (1985)]; and the Herpes Simplex virus glycoprotein D (gD)tag and its antibody substrate [Paborsky et al., Protein Engineering,3(6):547-553 (1990)]. In general, surface binding substrate moleculesinclude, but are not limited to, polyhistidine structures (His-tags)that bind nickel substrates, antigens that bind to surface substratescomprising antibody, haptens that bind to avidin substrate (e.g.,biotin) and CBP that binds to surface substrate comprising calmodulin.

Detection

In practicing the methods described herein, the detection of the statusof the one or more activatable elements can be carried out by a person,such as a technician in the laboratory. The detection of the status ofthe one or more activatable elements can be carried out using automatedsystems. In either case, the detection of the status of the one or moreactivatable elements for use according to the methods described hereincan be performed according to standard techniques and protocolswell-established in the art.

One or more activatable elements can be detected and/or quantified byany method that detects and/or quantitates the presence of theactivatable element of interest. Such methods may includeradioimmunoassay (RIA) or enzyme linked immunoabsorbance assay (ELISA),immunohistochemistry, immunofluorescent histochemistry with or withoutconfocal microscopy, reversed phase assays, homogeneous enzymeimmunoassays, and related non-enzymatic techniques, Western blots, FarWestern, Northern Blot, Southern blot, whole cell staining,immunoelectronmicroscopy, nucleic acid amplification, gene array,protein array, mass spectrometry, nucleic acid sequencing, nextgeneration sequencing, patch clamp, 2-dimensional gel electrophoresis,differential display gel electrophoresis, microsphere-based multiplexprotein assays, label-free cellular assays and flow cytometry, etc. U.S.Pat. No. 4,568,649 describes ligand detection systems, which employscintillation counting. These techniques are particularly useful formodified protein parameters. Cell readouts for proteins and other celldeterminants can be obtained using fluorescent or otherwise taggedreporter molecules. Flow cytometry methods are useful for measuringintracellular parameters. See U.S. patent application Ser. No.10/898,734 and Shulz et al., Current Protocols in Immunology, 2007,78:8.17.1-20 which are incorporated by reference in their entireties.

In some embodiments, methods are provided for determining the activationlevel on an activatable element for a single cell. The methods maycomprise analyzing cells by flow cytometry on the basis of theactivation level of at least two activatable elements. Binding elements(e.g., activation state-specific antibodies) can be used to analyzecells on the basis of activatable element activation level, and can bedetected as described below. Non-binding element systems as describedabove can be used in any system described herein.

When using fluorescent labeled components in the methods andcompositions described herein, different types of fluorescent monitoringsystems, e.g., cytometric measurement device systems, can be used. Insome embodiments, flow cytometric systems are used or systems dedicatedto high throughput screening, e.g., 96 well or greater microtiterplates. Methods of performing assays on fluorescent materials are wellknown in the art and are described in, e.g., Lakowicz, J. R., Principlesof Fluorescence Spectroscopy, New York: Plenum Press (1983); Herman, B.,Resonance energy transfer microscopy, in: Fluorescence Microscopy ofLiving Cells in Culture, Part B, Methods in Cell Biology, vol. 30, ed.Taylor, D. L. & Wang, Y.-L., San Diego: Academic Press (1989), pp.219-243; Turro, N. J., Modern Molecular Photochemistry, Menlo Park:Benjamin/Cummings Publishing Col, Inc. (1978), pp. 296-361.

Fluorescence in a sample can be measured using a fluorimeter. Ingeneral, excitation radiation, from an excitation source having a firstwavelength, passes through excitation optics. The excitation opticscause the excitation radiation to excite the sample. In response,fluorescent proteins in the sample emit radiation that has a wavelengththat is different from the excitation wavelength. Collection optics thencollect the emission from the sample. The device can include atemperature controller to maintain the sample at a specific temperaturewhile it is being scanned. According to one embodiment, a multi-axistranslation stage moves a microtiter plate holding a plurality ofsamples in order to position different wells to be exposed. Themulti-axis translation stage, temperature controller, auto-focusingfeature, and electronics associated with imaging and data collection canbe managed by an appropriately programmed digital computer. The computeralso can transform the data collected during the assay into anotherformat for presentation. In general, known robotic systems andcomponents can be used.

Other methods of detecting fluorescence may also be used, e.g., Quantumdot methods (see, e.g., Goldman et al., J. Am. Chem. Soc. (2002)124:6378-82; Pathak et al. J. Am. Chem. Soc. (2001) 123:4103-4; andRemade et al., Proc. Natl. Sci. USA (2000) 18:553-8, each expresslyincorporated herein by reference) as well as confocal microscopy. Ingeneral, flow cytometry involves the passage of individual cells throughthe path of a laser beam. The scattering the beam and excitation of anyfluorescent molecules attached to, or found within, the cell is detectedby photomultiplier tubes to create a readable output, e.g., size,granularity, or fluorescent intensity.

The detecting, sorting, or isolating step of the methods describedherein can entail fluorescence-activated cell sorting (FACS) techniques,where FACS is used to select cells from the population containing aparticular surface marker, or the selection step can entail the use ofmagnetically responsive particles as retrievable supports for targetcell capture and/or background removal. A variety of FACS systems areknown in the art and can be used in the methods described herein (seee.g., WO99/54494, filed Apr. 16, 1999; U.S. Ser. No. 20010006787, filedJul. 5, 2001, each expressly incorporated herein by reference).

In some embodiments, a FACS cell sorter (e.g., a FACSVantage™ CellSorter, Becton Dickinson Immunocytometry Systems, San Jose, Calif.) isused to sort and collect cells that may be used as a modulator or as apopulation of reference cells. In some embodiments, the modulator orreference cells are first contacted with fluorescent-labeled bindingelements (e.g., antibodies) directed against specific elements. In suchan embodiment, the amount of bound binding element on each cell can bemeasured by passing droplets containing the cells through the cellsorter. By imparting an electromagnetic charge to droplets containingthe positive cells, the cells can be separated from other cells. Thepositively selected cells can then be harvested in sterile collectionvessels. These cell-sorting procedures are described in detail, forexample, in the FACSVantage™. Training Manual, with particular referenceto sections 3-11 to 3-28 and 10-1 to 10-17, which is hereby incorporatedby reference in its entirety.

In another embodiment, positive cells can be sorted using magneticseparation of cells based on the presence of an isoform of anactivatable element. In such separation techniques, cells to bepositively selected can be first contacted with a specific bindingelement (e.g., an antibody or reagent that binds an isoform of anactivatable element). The cells can then be contacted with retrievableparticles (e.g., magnetically responsive particles) that can be coupledwith a reagent that binds the specific element. The cell-bindingelement-particle complex can then be physically separated fromnon-positive or non-labeled cells, for example, using a magnetic field.When using magnetically responsive particles, the positive or labeledcells can be retained in a container using a magnetic filed while thenegative cells are removed. These and similar separation procedures aredescribed, for example, in the Baxter Immunotherapy Isolex trainingmanual which is hereby incorporated in its entirety.

In some embodiments, methods for the determination of a receptor elementactivation state profile for a single cell are provided. The methods cancomprise providing a population of cells and analyzing the population ofcells by flow cytometry. Cells can be analyzed on the basis of theactivation level of at least one activatable element. In someembodiments, cells are analyzed on the basis of the activation level ofat least two activatable elements.

In some embodiments, a multiplicity of activatable elementactivation-state antibodies are used to simultaneously determine theactivation level of a multiplicity of elements.

In some embodiments, cell analysis by flow cytometry on the basis of theactivation level of at least two elements is combined with adetermination of other flow cytometry readable outputs, such as thepresence of surface markers, granularity and cell size to provide acorrelation between the activation level of a multiplicity of elementsand other cell qualities measurable by flow cytometry for single cells.

The ordering of element clustering events in signal transduction is alsoprovided. For example, an element clustering and activation hierarchycan be constructed based on the correlation of levels of clustering andactivation of a multiplicity of elements within single cells. Orderingcan be accomplished by comparing the activation level of a cell or cellpopulation with a control at a single time point, or by comparing cellsat multiple time points to observe subpopulations arising out of theothers.

As will be appreciated, these methods provide for the identification ofdistinct signaling cascades for both artificial and stimulatoryconditions in cell populations, such as peripheral blood mononuclearcells, or naive and memory lymphocytes.

Cells can be dispersed into a single cell suspension, e.g., by enzymaticdigestion with a suitable protease, e.g., collagenase, dispase, etc; andthe like. An appropriate solution can be used for dispersion orsuspension. Such solution will generally be a balanced salt solution,e.g., normal saline, PBS, Hanks balanced salt solution, etc.,conveniently supplemented with fetal calf serum or other naturallyoccurring factors, in conjunction with an acceptable buffer at lowconcentration, generally from 5-25 mM. Convenient buffers include HEPES,phosphate buffers, lactate buffers, etc. The cells may be fixed, e.g.,with 3% paraformaldehyde, and can be permeabilized, e.g., with ice coldmethanol; HEPES-buffered PBS containing 0.1% saponin, 3% BSA; coveringfor 2 min in acetone at −200° C.; and the like as known in the art andaccording to the methods described herein.

In some embodiments, one or more cells are contained in a well of a 96well plate or other commercially available multiwell plate. In someembodiments, the reaction mixture or cells are in a cytometricmeasurement device. Other multiwell plates useful include, but are notlimited to 384 well plates and 1536 well plates. Still other vessels forcontaining the reaction mixture or cells will be apparent to the skilledartisan.

The addition of the components of the assay for detecting the activationlevel or activity of an activatable element, or modulation of suchactivation level or activity, may be sequential or in a predeterminedorder or grouping under conditions appropriate for the activity that isassayed for. Such conditions are described here and known in the art.Moreover, further guidance is provided below (see, e.g., in theExamples).

In some embodiments, the activation level of an activatable element ismeasured using Inductively Coupled Plasma Mass Spectrometer (ICP-MS). Abinding element that has been labeled with a specific element can bindto the activatable element. When the cell is introduced into the ICP, itcan be atomized and ionized. The elemental composition of the cell,including the labeled binding element that is bound to the activatableelement, can be measured. The presence and intensity of the signalscorresponding to the labels on the binding element can indicate thelevel of the activatable element on that cell (Tanner et al.Spectrochimica Acta Part B: Atomic Spectroscopy, 2007 March;62(3):188-195.).

The instant methods and compositions can be used in a variety of otherassay formats in addition to flow cytometry analysis. For example, achip analogous to a DNA chip can be used in the methods provided herein.Arrayers and methods for spotting nucleic acids on a chip in aprefigured array are known. In addition, protein chips and methods forsynthesis are known. These methods and materials may be adapted for thepurpose of affixing activation state binding elements to a chip in aprefigured array. In some embodiments, such a chip comprises amultiplicity of element activation state binding elements, and is usedto determine an element activation state profile for elements present onthe surface of a cell. See U.S. Pat. No. 5,744,934.

In some embodiments confocal microscopy can be used to detect activationprofiles for individual cells. Confocal microscopy can use serialcollection of light from spatially filtered individual specimen points,which can then be electronically processed to render a magnified imageof the specimen. The signal processing involved confocal microscopy canhave the additional capability of detecting labeled binding elementswithin single cells; accordingly in this embodiment the cells can belabeled with one or more binding elements. In some embodiments thebinding elements used in connection with confocal microscopy areantibodies conjugated to fluorescent labels; however other bindingelements, such as other proteins or nucleic acids are also possible.

In some embodiments, the methods and compositions provided herein can beused in conjunction with an “In-Cell Western Assay.” In such an assay,cells can be initially grown in standard tissue culture flasks usingstandard tissue culture techniques. Once grown to optimum confluency,the growth media can be removed and cells can be washed and trypsinized.The cells can then be counted and volumes sufficient to transfer theappropriate number of cells can be aliquoted into microwell plates(e.g., Nunc™ 96 Microwell™ plates). The individual wells can then begrown to optimum confluency in complete media whereupon the media can bereplaced with serum-free media. At this point controls can be untouched,but experimental wells can be incubated with a modulator, e.g., EGF.After incubation with the modulator cells can be fixed and stained withlabeled antibodies to the activation elements being investigated. Oncethe cells are labeled, the plates can be scanned using an imager such asthe Odyssey Imager (LiCor, Lincoln Nebr.) using techniques described inthe Odyssey Operator's Manual v1.2., which is hereby incorporated in itsentirety. Data obtained by scanning of the multiwell plate can beanalyzed and activation profiles determined as described below.

In some embodiments, the detecting is by high pressure liquidchromatography (HPLC), for example, reverse phase HPLC. In a furtherembodiment, the detecting is by mass spectrometry.

These instruments can fit in a sterile laminar flow or fume hood, or canbe enclosed, self-contained systems, for cell culture growth andtransformation in multi-well plates or tubes and for hazardousoperations. The living cells may be grown under controlled growthconditions, with controls for temperature, humidity, and gas for timeseries of the live cell assays. Automated transformation of cells andautomated colony pickers may facilitate rapid screening of desiredcells.

Flow cytometry or capillary electrophoresis formats can be used forindividual capture of magnetic and other beads, particles, cells, andorganisms.

Flexible hardware and software allow instrument adaptability formultiple applications. The software program modules allow creation,modification, and running of methods. The system diagnostic modulesallow instrument alignment, correct connections, and motor operations.Customized tools, labware, and liquid, particle, cell and organismtransfer patterns allow different applications to be performed.Databases allow method and parameter storage. Robotic and computerinterfaces allow communication between instruments.

In some embodiments, the methods provided herein include the use ofliquid handling components. The liquid handling systems can includerobotic systems comprising any number of components. In addition, any orall of the steps outlined herein may be automated; thus, for example,the systems may be completely or partially automated.

There are a wide variety of components which can be used, including, butnot limited to, one or more robotic arms; plate handlers for thepositioning of microplates; automated lid or cap handlers to remove andreplace lids for wells on non-cross contamination plates; tip assembliesfor sample distribution with disposable tips; washable tip assembliesfor sample distribution; 96 well loading blocks; cooled reagent racks;microtiter plate pipette positions (optionally cooled); stacking towersfor plates and tips; and computer systems. See U.S. Ser. No. 61/048,657which is incorporated by reference in its entirety.

Fully robotic or microfluidic systems include automated liquid-,particle-, cell- and organism-handling including high throughputpipetting to perform all steps of screening applications. This includesliquid, particle, cell, and organism manipulations such as aspiration,dispensing, mixing, diluting, washing, accurate volumetric transfers;retrieving, and discarding of pipet tips; and repetitive pipetting ofidentical volumes for multiple deliveries from a single sampleaspiration. These manipulations are cross-contamination-free liquid,particle, cell, and organism transfers. This instrument performsautomated replication of microplate samples to filters, membranes,and/or daughter plates, high-density transfers, full-plate serialdilutions, and high capacity operation.

In some embodiments, chemically derivatized particles, plates,cartridges, tubes, magnetic particles, or other solid phase matrix withspecificity to the assay components are used. The binding surfaces ofmicroplates, tubes or any solid phase matrices include non-polarsurfaces, highly polar surfaces, modified dextran coating to promotecovalent binding, antibody coating, affinity media to bind fusionproteins or peptides, surface-fixed proteins such as recombinant proteinA or G, nucleotide resins or coatings, and other affinity matrix areuseful in the methods described herein.

In some embodiments, platforms for multi-well plates, multi-tubes,holders, cartridges, minitubes, deep-well plates, microfuge tubes,cryovials, square well plates, filters, chips, optic fibers, beads, andother solid-phase matrices or platform with various volumes areaccommodated on an upgradable modular platform for additional capacity.This modular platform includes a variable speed orbital shaker, andmulti-position work decks for source samples, sample and reagentdilution, assay plates, sample and reagent reservoirs, pipette tips, andan active wash station. In some embodiments, the methods provided hereininclude the use of a plate reader. See U.S. Ser. No. 61/048,657.

In some embodiments, thermocycler and thermoregulating systems are usedfor stabilizing the temperature of heat exchangers such as controlledblocks or platforms to provide accurate temperature control ofincubating samples from 0° C. to 100° C.

In some embodiments, interchangeable pipet heads (single ormulti-channel) with single or multiple magnetic probes, affinity probes,or pipetters robotically manipulate the liquid, particles, cells, andorganisms. Multi-well or multi-tube magnetic separators or platformsmanipulate liquid, particles, cells, and organisms in single or multiplesample formats.

In some embodiments, the instrumentation includes a detector, which canbe a wide variety of different detectors, depending on the labels andassay. In some embodiments, useful detectors include a microscope(s)with multiple channels of fluorescence; plate readers to providefluorescent, ultraviolet and visible spectrophotometric detection withsingle and dual wavelength endpoint and kinetics capability,fluorescence resonance energy transfer (FRET), luminescence, quenching,two-photon excitation, and intensity redistribution; CCD cameras tocapture and transform data and images into quantifiable formats; and acomputer workstation.

In some embodiments, the robotic apparatus includes a central processingunit which communicates with a memory and a set of input/output devices(e.g., keyboard, mouse, monitor, printer, etc.) through a bus. Again, asoutlined below, this may be in addition to or in place of the CPU forthe multiplexing devices described herein. The general interactionbetween a central processing unit, a memory, input/output devices, and abus is known in the art. Thus, a variety of different procedures,depending on the experiments to be run, can be stored in the CPU memory.See U.S. Ser. No. 61/048,657 which is incorporated by reference in itsentirety.

These robotic fluid handling systems can utilize any number of differentreagents, including buffers, reagents, samples, washes, assay componentssuch as label probes, etc.

Any of the steps described herein can be performed by a computer programproduct that comprises a computer executable logic that is recorded on acomputer readable medium. For example, the computer program can executesome or all of the following functions: (i) exposing differentpopulation of cells to one or more modulators, (ii) exposing differentpopulation of cells to one or more binding elements, (iii) detecting anactivation level of one or more activatable elements, (iv) making adiagnosis or prognosis based on the activation level of one or moreactivatable elements in the different populations, (v) comparing asignaling profile of a normal cell to a signaling profile from a cellfrom an individual, e.g., a test subject (e.g., an undiagnosedindividual), (vi) determining if the cell from the test subject e.g., anundiagnosed individual, is normal based on the comparing in (v), (vii)generating a report, (viii) modeling the dynamic response of nodes overtime, (ix) characterizing the cells based on the activation levels overtime (the “activation profile” of a node), (x) generating metrics suchas slope or expressed using linear equations, (xi) segregating singlecells into discrete cell populations, (xii) segregating a cellpopulation based on a common characteristic including but not limitedto: cell type, cell morphology and expression of a gene or protein,(xiii) simultaneously measuring the activation levels of severalactivatable elements in single cells, (xiv) measuring other markers(e.g., cell surface proteins, activatable elements) that can be used todetermine a type of the cell, (xv) gating cells, (xvi) quantifyingranges of signaling of activatable elements within each cellsub-population, (xvii) describing signaling ranges within eachsub-population for normal and diseased states by statistical methodssuch as histograms, boxplots, radar plots, a line graph with error bars,a bar and whisker plot, a circle plot, a heat map, and/or a bar graph,(xviii) using multivariate statistical methods, such as regression,random forests, or clustering, to summarize the ranges of signalingacross all cell sub-populations for normal and diseased states, (xviv)normalizing a test sample based on a sample grouping or characteristic(e.g., race, age, ethnicity, or gender).

In some embodiments, methods include use of one or more computers in acomputer system (1600). In some embodiments, the computer system isintegrated into and is part of an analysis system, like a flowcytometer. In other embodiments, the computer system is connected to orported to an analysis system. In some embodiments, the computer systemis connected to an analysis system by a network connection. The computermay include a monitor 1607 or other graphical interface for displayingdata, results, billing information, marketing information (e.g.,demographics), customer information, or sample information. The computermay also include means for data or information input, such as a keyboard1615 or mouse 1616. The computer may include a processing unit 1601 andfixed 1603 or removable 1611 media or a combination thereof. Thecomputer may be accessed by a user in physical proximity to thecomputer, for example via a keyboard and/or mouse, or by a user 1622that does not necessarily have access to the physical computer through acommunication medium 1605 such as a modem, an internet connection, atelephone connection, or a wired or wireless communication signalcarrier wave. In some cases, the computer may be connected to a server1609 or other communication device for relaying information from a userto the computer or from the computer to a user. In some cases, the usermay store data or information obtained from the computer through acommunication medium 1605 on media, such as removable media 1612.

The computer executable logic can work in any computer that may be anyof a variety of types of general-purpose computers such as a personalcomputer, network server, workstation, or other computer platform now orlater developed. In some embodiments, a computer program product isdescribed comprising a computer usable medium having the computerexecutable logic (computer software program, including program code)stored therein. The computer executable logic can be executed by aprocessor, causing the processor to perform functions described herein.In other embodiments, some functions are implemented primarily inhardware using, for example, a hardware state machine. Implementation ofthe hardware state machine so as to perform the functions describedherein will be apparent to those skilled in the relevant arts. In someembodiments, a system is provided for executing computer executablelogical, wherein the system comprises a computer.

The program can provide a method of determining the status of anindividual by accessing data that reflects the activation level of oneor more activatable elements in the reference population of cells.

Conditions

The methods described herein can be applicable to any condition in anindividual involving, indicated by, and/or arising from, in whole or inpart, altered physiological status in cells. The term “physiologicalstatus” includes mechanical, physical, and biochemical functions in acell. In some embodiments, the physiological status of a cell isdetermined by measuring characteristics of at least one cellularcomponent of a cellular pathway in cells from different populations(e.g., different cell networks). Cellular pathways are well known in theart. In some embodiments the cellular pathway is a signaling pathway.Signaling pathways are also well known in the art (see, e.g., Hunter T.,Cell 100(1): 113-27 (2000); Cell Signaling Technology, Inc., 2002Catalogue, Pathway Diagrams pgs. 232-253; Weinberg, Chapter 6, Thebiology of Cancer, 2007; and Blume-Jensen and Hunter, Nature, vol 411,17 May 2001, p 355-365). A condition involving or characterized byaltered physiological status may be readily identified, for example, bydetermining the state of one or more activatable elements in cells fromdifferent populations, as taught herein.

In certain embodiments, the condition is a neoplastic, immunologic orhematopoietic condition. In some embodiments, the neoplastic,immunologic or hematopoietic condition is selected from the groupconsisting of solid tumors such as head and neck cancer including brain,thyroid cancer, breast cancer, lung cancer, mesothelioma, germ celltumors, ovarian cancer, liver cancer, gastric carcinoma, colon cancer,prostate cancer, pancreatic cancer, melanoma, bladder cancer, renalcancer, prostate cancer, testicular cancer, cervical cancer, endometrialcancer, myosarcoma, leiomyosarcoma and other soft tissue sarcomas,osteosarcoma, Ewing's sarcoma, retinoblastoma, rhabdomyosarcoma, Wilm'stumor, and neuroblastoma, sepsis, allergic diseases and disorders thatinclude but are not limited to allergic rhinitis, allergicconjunctivitis, allergic asthma, atopic eczema, atopic dermatitis, andfood allergy, immunodeficiencies including but not limited to severecombined immunodeficiency (SCID), hypereosiniphic syndrome, chronicgranulomatous disease, leukocyte adhesion deficiency I and II, hyper IgEsyndrome, Chediak Higashi, neutrophilias, neutropenias, aplasias,agammaglobulinemia, hyper-IgM syndromes, DiGeorge/Velocardial-facialsyndromes and Interferon gamma-TH1 pathway defects, autoimmune andimmune dysregulation disorders that include but are not limited torheumatoid arthritis, diabetes, systemic lupus erythematosus, Graves'disease, Graves ophthalmopathy, Crohn's disease, multiple sclerosis,psoriasis, systemic sclerosis, goiter and struma lymphomatosa(Hashimoto's thyroiditis, lymphadenoid goiter), alopecia aerata,autoimmune myocarditis, lichen sclerosis, autoimmune uveitis, Addison'sdisease, atrophic gastritis, myasthenia gravis, idiopathicthrombocytopenic purpura, hemolytic anemia, primary biliary cirrhosis,Wegener's granulomatosis, polyarteritis nodosa, and inflammatory boweldisease, allograft rejection and tissue destructive from allergicreactions to infectious microorganisms or to environmental antigens, andhematopoietic conditions that include but are not limited to Non-HodgkinLymphoma, Hodgkin or other lymphomas, acute or chronic leukemias,polycythemias, thrombocythemias, multiple myeloma or plasma celldisorders, e.g., amyloidosis and Waldenstrom's macroglobulinemia,myelodysplastic disorders, myeloproliferative disorders, myelofibroses,or atypical immune lymphoproliferations. In some embodiments, theneoplastic or hematopoietic condition is non-B lineage derived, such asAcute myeloid leukemia (AML), Chronic Myeloid Leukemia (CML), non-B cellAcute lymphocytic leukemia (ALL), non-B cell lymphomas, myelodysplasticdisorders, myeloproliferative disorders, myelofibroses, polycythemias,thrombocythemias, or non-B atypical immune lymphoproliferations, ChronicLymphocytic Leukemia (CLL), B lymphocyte lineage leukemia, B lymphocytelineage lymphoma, Multiple Myeloma, or plasma cell disorders, e.g.,amyloidosis or Waldenstrom's macroglobulinemia.

In some embodiments, the neoplastic or hematopoietic condition is non-Blineage derived. Examples of non-B lineage derived neoplastic orhematopoietic condition include, but are not limited to, Acute myeloidleukemia (AML), Chronic Myeloid Leukemia (CML), non-B cell Acutelymphocytic leukemia (ALL), non-B cell lymphomas, myelodysplasticdisorders, myeloproliferative disorders, myelofibroses, polycythemias,thrombocythemias, and non-B atypical immune lymphoproliferations.

In some embodiments, the neoplastic or hematopoietic condition is aB-Cell or B cell lineage derived disorder. Examples of B-Cell or B celllineage derived neoplastic or hematopoietic condition include but arenot limited to Chronic Lymphocytic Leukemia (CLL), B lymphocyte lineageleukemia, B lymphocyte lineage lymphoma, Multiple Myeloma, and plasmacell disorders, including amyloidosis and Waldenstrom'smacroglobulinemia.

Other conditions can include, but are not limited to, cancers such asgliomas, lung cancer, colon cancer and prostate cancer. Specificsignaling pathway alterations have been described for many cancers,including loss of PTEN and resulting activation of Akt signaling inprostate cancer (Whang Y E. Proc Natl Acad Sci USA Apr. 28, 1998;95(9):5246-50), increased IGF-1 expression in prostate cancer (Schaeferet al., Science Oct. 9, 1998, 282: 199a), EGFR overexpression andresulting ERK activation in glioma cancer (Thomas C Y. Int J Cancer Mar.10, 2003; 104(1):19-27), expression of HER2 in breast cancers (Menard etal. Oncogene. Sep. 29 2003, 22(42):6570-8), and APC mutation andactivated Wnt signaling in colon cancer (Bienz M. Curr Opin Genet Dev1999 October, 9(5):595-603).

In certain embodiments, the condition is neurological condition, e.g.,Alzheimer's disease, Bell's Palsy, aphasia, Creutzfeldt-Jakob Disease(CJD), cerebrovascular disease, encephalitis, epilepsy, Huntington'sdisease, trigeminal neuralgia, migraine, Parkinson's disease,amyotrophic lateral sclerosis, Guillain-Barre syndrome, musculardystrophy, spastic paraplegia, Von Hippel-Lindau disease (VHL), autism,dyslexia, narcolepsy, restless legs syndrome, Meniere's disease, ordementia.

Diseases other than cancer involving altered physiological status arealso encompassed by the methods described herein. For example, it hasbeen shown that diabetes involves underlying signaling changes, namelyresistance to insulin and failure to activate downstream signalingthrough IRS (Burks D J, White M F. Diabetes 2001 February; 50 Suppl1:S140-5). Similarly, cardiovascular disease has been shown to involvehypertrophy of the cardiac cells involving multiple pathways such as thePKC family (Malhotra A. Mol Cell Biochem 2001 September; 225(1-):97-107). Inflammatory diseases, such as rheumatoid arthritis, areknown to involve the chemokine receptors and disrupted downstreamsignaling (D'Ambrosio D. J Immunol Methods 2003 February; 273(1-2):3-13). The methods described herein are not limited to diseasespresently known to involve altered cellular function, but includediseases subsequently shown to involve physiological alterations oranomalies.

Kits

In some embodiments, kits are provided. Kits may comprise one or more ofthe state-specific binding elements described herein, such asphospho-specific antibodies. A kit may also include other reagents, suchas modulators, fixatives, containers, plates, buffers, therapeuticagents, instructions, and the like.

In some embodiments, the kit comprises one or more of thephospho-specific antibodies specific for the proteins selected from thegroup consisting of PI3-Kinase (p85, p110a, p110b, p110d), Jak1, Jak2,SOCs, Rac, Rho, Cdc42, Ras-GAP, Vav, Tiam, Sos, Dbl, Nck, Gab, PRK,SHP1, and SHP2, SHIP1, SHIP2, sSHIP, PTEN, Shc, Grb2, PDK1, SGK, Akt1,Akt2, Akt3, TSC1, 2, Rheb, mTor, 4EBP-1, p70S6Kinase, S6, LKB-1, AMPK,PFK, Acetyl-CoAa Carboxylase, DokS, Rafs, Mos, Tpl2, MEK1/2, MLK3, TAK,DLK, MKK3/6, MEKK1, 4, MLK3, ASK1, MKK4/7, SAPK/JNK1, 2, 3, p38s,Erk1/2, Syk, Btk, BLNK, LAT, ZAP70, Lck, Cbl, SLP-76, PLCγ₁, PLCγ2,STAT1, STAT 3, STAT 4, STAT 5, STAT 6, FAK, p130CAS, PAKs, LIMK1/2,Hsp90, Hsp70, Hsp27, SMADs, Rel-A (p65-NFKB), CREB, Histone H₂B, HATs,HDACs, PKR, Rb, Cyclin D, Cyclin E, Cyclin A, Cyclin B, P16, p14Arf,p27KIP, p21CIP, Cdk4, Cdk6, Cdk7, Cdk1, Cdk2, Cdk9, Cdc25, A/B/C, Abl,E2F, FADD, TRADD, TRAF2, Myd88, BAD, Bcl-2, Mcl-1, Bcl-XL, Caspase 2,Caspase 3, Caspase 6, Caspase 7, Caspase 8, Caspase 9, IAPs, Smac,Fodrin, Actin, Src, Lyn, Fyn, Lck, NIK, IκB, p65(RelA), IKKα, PKA, PKCα,PKCβ, PKCθ, PKCδ, CAMK, Elk, AFT, Myc, Egr-1, NFAT, ATF-2, Mdm2, p53,DNA-PK, Chk1, Chk2, ATM, ATR, {tilde over (B)}catenin, CrkL, GSK3α,GSK3β, and FOXO. In some embodiments, the kit comprises one or more ofthe phospho-specific antibodies specific for the proteins selected fromthe group consisting of Erk, Erk1, Erk2, Syk, Zap70, Lck, Btk, BLNK,Cbl, PLCγ2, Akt, Rel A, p38, S6. In some embodiments, the kit comprisesone or more of the phospho-specific antibodies specific for the proteinsselected from the group consisting of Akt1, Akt2, Akt3, SAPK/JNK1, 2, 3,p38s, Erk1/2, Syk, ZAP70, Btk, BLNK, Lck, PLCγ, PLCγ2, STAT1, STAT 3,STAT 4, STAT 5, STAT 6, CREB, Lyn, p-S6, Cbl, NF-kB, GSK3β, CARMA/Bcl10and Tcl-1.

The state-specific binding element can be conjugated to a solid supportand to detectable groups directly or indirectly. The reagents may alsoinclude ancillary agents such as buffering agents and stabilizingagents, e.g., polysaccharides and the like. The kit may further include,where necessary, other members of the signal-producing system of whichsystem the detectable group is a member (e.g., enzyme substrates),agents for reducing background interference in a test, control reagents,apparatus for conducting a test, and the like. The kit may be packagedin any suitable manner, typically with all elements in a singlecontainer along with a sheet of printed instructions for carrying outthe test.

Such kits enable the detection of activatable elements by sensitivecellular assay methods, such as IHC and flow cytometry, which aresuitable for the clinical detection, prognosis, and screening of cellsand tissue from patients, such as leukemia patients, having a diseaseinvolving altered pathway signaling Such kits may additionally compriseone or more therapeutic agents. The kit may further comprise a softwarepackage for data analysis of the physiological status, which may includereference profiles for comparison with the test profile.

Such kits may also include information, such as scientific literaturereferences, package insert materials, clinical trial results, and/orsummaries of these and the like, which indicate or establish theactivities and/or advantages of the composition, and/or which describedosing, administration, side effects, drug interactions, or otherinformation useful to the health care provider. Such information may bebased on the results of various studies, for example, studies usingexperimental animals involving in vivo models and studies based on humanclinical trials. Kits described herein can be provided, marketed and/orpromoted to health providers, including physicians, nurses, pharmacists,formulary officials, and the like. Kits may also, in some embodiments,be marketed directly to the consumer.

Generation of Dynamic Activation State Data

In some embodiments, the activation levels of a discrete cell populationor a discrete subpopulation of cells may be measured at multiple timeintervals following treatment with a modulator to generate “dynamicactivation state data” (also referred to herein as kinetic activationstate data). In these embodiments, a sample or sub-sample (e.g., patientsample) is divided into aliquots which are then treated with one or moremodulators. The different aliquots can then be subject to treatment witha fixing agent at the different time intervals. For instance, an aliquotthat is to be measured at 5 minutes can be treated with one or moremodulators and can then be subjected to a treatment with a fixing agentafter 5 minutes. The time intervals can vary greatly and can range fromminutes (e.g., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 minutes) tohours (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 6, 1718, 19, 20, 21, 22, 23 hours) to days (e.g., 24 hours, 48 hours, 72hours) or any combination thereof. Cells may also be treated withdifferent concentrations of a modulator.

In some embodiments, the activation state data may be analyzed toidentify discrete cell populations and then further analyzed tocharacterize the response of the different discrete cell populations tothe modulator over time. The activation state data may be temporallymodeled to characterize the dynamic response of the activatable elementsto the stimulation with the modulator. Modeling the dynamic response tomodulation can provide a better understanding of the patho-physiology ofa disease or prognostic status or a response to treatment. Modeling thedynamic response of normal cells to a modulator is shown in FIG. 3 anddiscussed below with respect to Example 6. Additionally, themodulator-induced activation levels of a discrete population of cellsover time associated with a disease status may be compared with othersamples to identify activation levels that represent an aberrantresponse to a modulator at specific time points. Aberrant response to amodulator may be associated with health status, a prognostic status, acytogenetic status or predicted therapeutic response. Having activationlevels at different time points is beneficial because the maximaldifferential response between samples associated with different statusesmay be observed as early as 5 minutes after treatment with a modulatorand as late as 72 hours after treatment with a modulator.

The modulator-induced response of the different discrete cellpopulations may be modeled to further understand communication betweenthe discrete cell populations that are associated with disease. Forexample, an increased phosphorylation of an activatable element in afirst cell population at an earlier time point may have a causal effecton the phosphorylation of a second activatable element in a second cellpopulation at a later time point. These causal associations may bemodeled using Bayesian Networks or temporal models. These causalassociations may be identified using unsupervised learning techniquessuch as principle components analysis and/or clustering. Causalassociations between activation levels in different cell populations mayrepresent communications between cellular networks over time. Thesecommunications may provide insight into the mechanism of drug response,cancer progression and carcinogenesis. Therefore, the identification andcharacterization of these communications allows for the development ofdiagnostics which can accurately predict drug response, therapeutic andearly stage detection.

In some embodiments, the activation state data at a first time point iscomputationally analyzed (e.g., through binning or gating as describedbelow) to determine discrete populations of cells. The discretepopulations of cells are subsequently analyzed individually over theremaining time points to identify sub-populations of cells withdifferent response to a modulator. Differential response over timewithin a same population of cells may be modeled using methods such astemporal modeling or hyper-spatial modeling as described in U.S. PatentApplication 61/317,817 and below. These methods may allow the modelingof a single discrete cell population over time or multiple discrete cellpopulations over time.

In another embodiment, the activation state data is computationalanalyzed at all of the time points to determine discrete populations ofcells. The discrete populations of cells can then be modeled in order todetermine consistent membership in a discrete population of cells overtime. In this way, the populations of cells are not characterized by theactivation levels of modulators at a single time point, but rather canbe determined based on the activation levels of modulators at multipletime points. Both gating and binning may be used to first segregate theactivation state data for cell populations at all of the time points.Based on the segregated cell populations at the various time points,discrete cell populations may be identified. This technique works wellusing gating or semi-supervised identification of discrete cellpopulations, and the technique can be used with unsupervisedidentification of discrete cell populations such as the methodsdescribed in U.S. Publication No. 2009/0307248 and below.

Computational Identification of Cell Populations

In some embodiments, the activation state data of a cell population isdetermined by contacting the cell population with one or moremodulators, generating activation state data for the cell population andusing computational techniques to identify one or more discrete cellpopulations based on the data. These techniques can be implemented usingcomputers comprising memory and hardware. In one embodiment, algorithmsfor generating metrics based on raw activation state data are stored inthe memory of a computer and executed by a processor of a computer.These algorithms can be used in conjunction with gating and binningalgorithms, which can also be stored and executed by a computer, toidentify the discrete cell populations.

The data can be analyzed using various metrics. For example, the medianfluorescence intensity (MFI) can be computed for each activatableelement from the intensity levels for the cells in the cell populationgate. The MFI values can then be used to compute a variety of metrics bycomparing them to the various baseline or background values, e.g., theunstimulated condition, autofluorescence, and isotype control. Thefollowing metrics are examples of metrics that can be used in themethods described herein: 1) a metric that measures the difference inthe log of the median fluorescence value between an unstimulatedfluorochrome-antibody stained sample and a sample that has not beentreated with a stimulant or stained (log(MFI_(Unstimulated Stained))−log(MFI_(Gated Unstained))), 2) a metric that measures the difference inthe log of the median fluorescence value between a stimulatedfluorochrome-antibody stained sample and a sample that has not beentreated with a stimulant or stained (log (MFI_(Stimulated Stained))−log(MFI_(Gated Unstained))), 3) a metric that measures the change betweenthe stimulated fluorochrome-antibody stained sample and the unstimulatedfluorochrome-antibody stained sample log (MFI_(Stimulated Stained))−log(MFI_(Unstumulated Stained)), also called “fold change in medianfluorescence intensity”, 4) a metric that measures the percentage ofcells in a Quadrant Gate of a contour plot which measures multiplepopulations in one or more dimension 5) a metric that measures MFI ofphosphor positive population to obtain percentage positivity above thebackground and 6) use of multimodality and spread metrics for largesample population and for subpopulation analysis.

In a specific embodiment, the equivalent number of referencefluorophores value (ERF) is generated. The ERF is a transformed value ofthe median fluorescent intensity values. The ERF value is computed usinga calibration line determined by fitting observations of a standardizedset of &peak rainbow beads for all fluorescent channels to standardizedvalues assigned by the manufacturer. The ERF values for differentsamples can be combined in any way to generate different activationstate metric. Different metrics can include: 1) a fold value based onERF values for samples that have been treated with a modulator (ERF_(m))and samples that have not been treated with a modulator (ERF_(u)), log₂(ERF_(m)/ERF_(u)); 2) a total phospho value based on ERF values forsamples that have been treated with a modulator (ERF_(m)) and samplesfrom autofluorecsent wells (ERF_(a)), log₂ (ERF_(m)/ERF_(a)); 3) a basalvalue based on ERF values for samples that have not been treated with amodulator (ERF_(u)) and samples from autofluorescent wells (ERF_(a)),log₂ (ERF_(u)/ERF_(a)); 4) A Mann-Whitney statistic U_(u) comparing theERF_(m and) ERF_(u) values that has been scaled down to a unit interval(0,1) allowing inter-sample comparisons; 5) A Mann-Whitney statisticU_(u) comparing the ERF_(m) and ERF_(u) values that has been scaled downto a unit interval (0,1) allowing inter-sample comparisons; 5) aMann-Whitney statistic U_(a) comparing the ERF_(a) and ERF_(m) valuesthat has been scaled down to a unit interval (0,1); and 6) AMann-Whitney statistic U75. U75 is a linear rank statistic designed toidentify a shift in the upper quartile of the distribution of ERF_(m)and ERF_(u) values. ERF values at or below the 75^(th) percentile of theERF_(m) and ERF_(u) values are assigned a score of 0. The remainingERF_(m) and ERF_(u) values are assigned values between 0 and 1 as in theU_(u) statistic. For activatable elements that are surface markers oncells, the following metrics may be further generated: 1) a relativeprotein expression metric log 2(ERF_(stain))−log 2(ERF_(control)) basedon the ERF value for a stained sample (ERF_(stain)) and the ERF valuefor a control sample (ERF_(control)); and 2) A Mann-Whitney statistic Uicomparing the ERF_(m) and ERF_(i) values that has been scaled down to aunit interval (0,1), where the ERF_(i) values are derived from anisotype control.

The activation state data for the different markers can be “gated” inorder to identify discrete subpopulations of cells within the data. Ingating, activation state data can be used to identify discretesub-populations of cells with distinct activation levels of anactivatable element. These discrete sub-populations of cells cancorrespond to cell types, cell sub-types, cells in a disease or otherphysiological state and/or a population of cells having anycharacteristic in common.

In some embodiments, the activation state data is displayed as atwo-dimensional scatter-plot and the discrete subpopulations are “gated”or demarcated within the scatter-plot. According to the embodiment, thediscrete subpopulations may be gated automatically, manually or usingsome combination of automatic and manual gating methods. In someembodiments, a user can create or manually adjust the demarcations or“gates” to generate new discrete sub-populations of cells. Suitablemethods of gating discrete sub-populations of cells are described inU.S. patent application Ser. No. 12/501,295, the entirety of which isincorporated by reference herein, for all purposes.

In some embodiments, the homogenous cell populations are gated accordingto markers that are known to segregate different cell types or cellsub-types. In a specific embodiment, a user can identify discrete cellpopulations based on surface markers. For example, the user could lookat: “stem cell populations” by CD34+ CD38- or CD34+ CD33-expressingcells; memory CD4 T lymphocytes; e.g., CD4⁺ CD45RA⁺ CD29^(low) cells; ormultiple leukemic sub-clones based on CD33, CD45, HLA-DR, CD11b andanalyzing signaling in each discrete population/subpopulation. Inanother embodiment, a user may identify discrete cellpopulations/subpopulations based on intracellular markers, such astranscription factors or other intracellular proteins; based on afunctional assay (e.g., dye efflux assay to determine drugtransporter+cells or fluorescent glucose uptake) or based on otherfluorescent markers. In some embodiments, gates are used to identify thepresence of specific discrete populations and/or subpopulations inexisting independent data. The existing independent data can be datastored in a computer from a previous patient, or data from independentstudies using different patients.

In some embodiments, the homogenous cell populations/subpopulations areautomatically gated according to activation state data that segregatesthe cells into discrete populations. For example, an activatable elementthat is “on” or “off” in cells may be used to segregate the cellpopulation into two discrete subpopulations. In embodiments where thediscrete cell subpopulations are automatically identified, differentalgorithms may be used to identify discrete homogenous cellsubpopulations based on the activation state data. In a specificembodiment, a multi-resolution binning algorithm is used to iterativelyidentify discrete subpopulations of cells by partitioning the activationstate data. This algorithm is outlined in detail in U.S. Publication No.2009/0307248, which is incorporated herein in its entirety, for allpurposes. In one embodiment, the multi-resolution binning algorithm isused to identify rare or uniquely discrete cell populations byiteratively identifying vectors or “hyperplanes” that partitionactivation state data into finer resolution bins. Using iterativealgorithms such as multi-resolution binning algorithms, fine resolutionbins containing rare populations of cells may be identified. Forexample, activation state data for one or more markers may beiteratively binned to identify a small number of cells with an unusuallyhigh expression of a marker. Normally, these cells would be discarded as“outlier” data or during normalization of the data. However,multi-resolution binning allows the identification of activation statedata corresponding to rare populations of cells.

In different embodiments, gating can be used in different ways toidentify discrete cell populations. In one embodiment, “Outside-in”comparison of activation state data for individual samples or subset(e.g., patients in a trial) is used to identify discrete cellpopulations. In this embodiment, cell populations are homogenous orlineage gated in such a way as to create discrete sets of cellsconsidered to be homogenous based on a characteristic (e.g., cell type,expression, subtype, etc.). An example of sample-level comparison in anAML patient would be the identification of signaling profiles inlymphocytes (e.g., CD4 T cells, CD8 T cells and/or B cells),monocytes+granulocytes and leukemic blast and correlating the activationstate data of these populations with non-random distribution of clinicalresponses. This is considered an outside-in approach because thediscrete cell population of interest is pre-defined prior to the mappingand comparison of its profile to, e.g., a clinical outcome or theprofile of the populations in normal individuals.

In other embodiments, “Inside-out” comparison of activation state dataat the level of individual cells in a heterogeneous population is usedto identify discrete cell populations. An example of this method wouldbe the signal transduction state mapping of mixed hematopoietic cellsunder certain conditions and subsequent comparison of computationallyidentified cell clusters with lineage specific markers. This methodcould be considered an inside-out approach to single cell studies as itdoes not presume the existence of specific discrete cell populationsprior to classification. Suitable methods for inside-out identificationof discrete cell populations include the multi-resolution binningalgorithm described above. This approach can create discrete cellpopulations which, at least initially, can use multiple transientmarkers to enumerate and may never be accessible with a single cellsurface epitope. As a result, the biological significance of suchdiscrete cell populations can be difficult to determine. The mainadvantage of this unconventional approach is the unbiased tracking ofdiscrete cell populations without drawing potentially arbitrarydistinctions between lineages or cell types and the potential of usingthe activation state data of the different populations to determine thestatus of an individual.

Classifying and Characterizing Cell Network Based on Activation StateData Associated with Discrete Populations of Cells

When the activation state data associated with a plurality of discretecell populations has been identified, it can be useful to determinewhether activation state data is non-randomly distributed within thecategories such as disease status, therapeutic response, clinicalresponses, presence of gene mutations, and protein expression levels.Activation state data that are strongly associated with one or morediscrete cell populations with a specific characteristic (e.g., genemutation, disease status) can be used both to classify a cell accordingto the characteristic and to further characterize and understand thecell network communications underlying the pathophysiology of thecharacteristic. Activation state data that uniquely identifies adiscrete cell population associated with a cell network can serve tore-enforce or complement other activation state data that uniquelyidentifies another discrete cell population associated with the cellnetwork.

If activation state data is available for many discrete cellpopulations, activation state data that uniquely identifies a discretecell population may be identified using simple statistical tests, suchas the Student's t-test and the X² test. Similarly, if the activationstate data of two discrete cell populations within the experiment isthought to be related, the r² correlation coefficient from a linearregression can be used to represent the degree of this relationship.Other methods include Pearson and Spearman rank correlation. In someembodiments, correlation and statistical test algorithms will be storedin the memory of a computer and executed by a processor associated withthe computer.

In some embodiments, the invention provides methods for determiningwhether the activation state data of different discrete cell populationsis associated with a cellular network and/or a characteristic that canpotentially complement each other to improve the accuracy ofclassification. In these embodiments, the activation state data of thediscrete cell populations may be used generate a classifier for one ormore characteristics associated with the discrete cell populationsincluding but not limited to: therapeutic response, disease status anddisease prognosis. A classifier can be any type of statistical modelthat can be used to characterize a similarity between a sample and aclass of samples. Classifiers can comprise binary and multi-classclassifiers as in the traditional use of the term classifier.Classifiers can also comprise statistical models of activation levelsand variance in only one class of samples (e.g., normal individuals).These single-class classifiers can be applied to data, e.g., fromundiagnosed samples, to produce a similarity value, which can be used todetermine whether the undiagnosed sample belongs to the class of samples(e.g. by using a threshold similarity value). Any suitable method knownin the art can be used to generate the classifier. For example, simplestatistical tests can be used to generate a classifier. Examples, ofclassification algorithms that can be used to generate a classifierinclude, but are not limited to, Linear classifiers, Fisher's lineardiscriminant, ANOVA, Logistic regression, Naive Bayes classifier,Perceptron, Support vector machines, Quadratic classifiers, Kernelestimation, k-nearest neighbor, Boosting. Decision trees, Randomforests, Neural networks, Bayesian networks, Hidden Markov models, andLearning vector quantization. Thus, in some embodiments, different typesof classification algorithms may be used to generate the classifierincluding but not limited to: neural networks, support vector machines(SVMs), bagging, boosting and logistic regression. In some embodiments,the activation state data for different discrete populations associatedwith a same network and/or characteristic may be pooled beforegenerating a classifier that specifies which combinations of activationstate data associated with discrete cell populations can be used touniquely identify and classify cells according to the activatableelement.

In a specific embodiment, if the size of the activation state dataassociated with the discrete populations of cells is small, astraightforward corner classifier approach for picking combinations ofactivation state data that uniquely identifies the different discretecell populations can be adopted. Combinations of discrete cellpopulations' activation state data can also be tested for theirstability via a bootstrapping approach described below. In thisembodiment, a corners classification algorithm can be applied to thedata. The corners classifier is a rules-based algorithm for dividingsubjects into two classes (e.g. dichotomized response to a treatment)using one or more numeric variables (e.g. population/node combination).This method works by setting a threshold on each variable, and thencombining the resulting intervals (e.g., X<10, or Y>50) with theconjunction (and) operator (reference). This creates a rectangularregion that is expected to hold most members of the class previouslyidentified as the target (e.g., responders or non-responders oftreatment). Threshold values are chosen by minimizing an error criterionbased on the logit-transformed misclassification rate within each class.The method assumes only that the two classes (e.g. response or lack ofresponse to treatment) tend to have different locations along thevariables used, and is invariant under monotone transformations of thosevariables.

In some embodiments, computational methods of cross-validation are usedduring classifier generation to measure the accuracy of the classifierand prevent over-fitting of the classifier to the data. In a specificembodiment, bagging techniques, aka bootstrapped aggregation, are usedto internally cross-validate the results of the above statistical model.In this embodiment, re-samples are iteratively drawn from the originaldata and used to validate the classifier. Each classifier, e.g.combination of population/node, is fit to the resample, and used topredict the class membership of those patients who were excluded fromthe resample. The accuracy of false positive and false negativeclassifications is determined for each classifier.

After iteratively re-sampling the original data, each patient acquires alist of predicted class memberships based on classifiers that were fitusing other patients. Each patient's list is reduced to the fraction oftarget class predictions; members of the target class should havefractions near 1, unlike members of the other class. The set of suchfractions, along with the patient's true class membership, is used tocreate a Receiver Operator Curve and to calculate the area under the ROCcurve (herein referred to as the “AUC”).

In some embodiments, methods are provided for determining a status of anindividual such a disease status, therapeutic response, and/or clinicalresponses wherein the positive predictive value (PPV) is higher than 60,70, 80, 90, 95, or 99.9%. In some embodiments, methods are provided fordetermining a status of an individual such as disease status,therapeutic response, and/or clinical responses, wherein the PPV isequal or higher than 95%. In some embodiments, methods are provided fordetermining a status of an individual such a disease status, therapeuticresponse, and/or clinical responses, wherein the negative predictivevalue (NPV) is higher than 60, 70, 80, 90, 95, or 99.9%. In someembodiments, methods are provided for determining a status of anindividual such as disease status, therapeutic response, and/or clinicalresponses, wherein the NPV is higher than 85%.

In some embodiments, methods are provided for predicting risk of relapseat 2 years, wherein the PPV is higher than 60, 70, 80, 90, 95, or 99.9%.In some embodiments, methods are provided for predicting risk of relapseat 2 years, wherein the PPV is equal or higher than 95%. In someembodiments, methods are provided for predicting risk of relapse at 2years, wherein the NPV is higher than 60, 70, 80, 90, 95, or 99.9%. Insome embodiments, methods are provided for predicting risk of relapse at2 years, wherein the NPV is higher than 80%. In some embodiments,methods are provided for predicting risk of relapse at 5 years, whereinthe PPV is higher than 60, 70, 80, 90, 95, or 99.9%. In someembodiments, methods are provided for predicting risk of relapse at 5years, wherein the PPV is equal or higher than 95%. In some embodiments,methods are provided for predicting risk of relapse at 5 years, whereinthe NPV is higher than 60, 70, 80, 90, 95, or 99.9%. In someembodiments, methods are provided for predicting risk of relapse at 5years, wherein the NPV is higher than 80%. In some embodiments, methodsare provided for predicting risk of relapse at 10 years, wherein the PPVis higher than 60, 70, 80, 90, 95, or 99.9%. In some embodiments,methods are provided for predicting risk of relapse at 10 years, whereinthe PPV is equal or higher than 95%. In some embodiments, methods areprovided for predicting risk of relapse at 10 years, wherein the NPV ishigher than 60, 70, 80, 90, 95, or 99.9%. In some embodiments, methodsare provided for predicting risk of relapse at 10 years, wherein the NPVis higher than 80%.

In some embodiments, the p value in the analysis of the methodsdescribed herein is below 0.05, 04, 0.03, 0.02, 0.01, 0.009, 0.005, or0.001. In some embodiments, the p value is below 0.001. Thus in someembodiments, methods are provided for determining a status of anindividual such a disease status, therapeutic response, and/or clinicalresponses, wherein the p value is below 0.05, 04, 0.03, 0.02, 0.01,0.009, 0.005, or 0.001. In some embodiments, the p value is below 0.001.In some embodiments, methods are provided for determining a status of anindividual such a disease status, therapeutic response, and/or clinicalresponses, wherein the AUC value is higher than 0.5, 0.6, 07, 0.8 or0.9. In some embodiments, methods are provided for determining a statusof an individual such a disease status, therapeutic response, and/orclinical responses, wherein the AUC value is higher than 0.7. In someembodiments, methods are provided for determining a status of anindividual such a disease status, therapeutic response, and/or clinicalresponses, wherein the AUC value is higher than 0.8. In someembodiments, methods are provided for determining a status of anindividual such a disease status, therapeutic response, and/or clinicalresponses, wherein the AUC value is higher than 0.9.

In another embodiment, activation state data generated for a cellularnetwork over a series of time points can be used to identify activationstate data that represents unique communications within the cellularnetwork over time. The activation state data that represents uniquecommunications within the cellular network can be used to classify otheractivation state data associated with cell populations to determinewhether they are associated with a same characteristic as the cellularnetwork or determine if there are in a specific stage or phase in timethat is unique to a cellular network. For example, different discretepopulations of cells in a cellular network can be treated with a samemodulator and sub-sampled over a series of time points to determinecommunications between the discrete populations of cells that are uniqueto the stimulation with the modulator. Similarly, samples of differentdiscrete cell populations can be derived from patients over the courseof treatment and used to identify communications between the discretepopulations of cells that are unique to the course of treatment.

In one embodiment, the activation state data for a discrete cellpopulation at different time points can be modeled to represent dynamicinteractions between the discrete cell populations in a cell networksover time. The activation state data can be modeled using temporalmodels, Bayesian networks or some combination therefore. Suitablemethods of generating Bayesian networks are described in 11/338,957, theentirety of which is incorporated herein, for all purposes. Suitablemethods of generating temporal models of activation state data aredescribed in U.S. Patent Application 61/317,817, the entirety of whichis incorporated herein by reference. Different metrics may be generatedto describe the dynamic interactions including: derivatives, integrals,rate-of-change metrics, splines, state representations of activationstate data and Boolean representations of activation state data.

In embodiments where metrics and other values describing dynamicinteractions are generated, these values and metrics are used togenerate a classifier. As outlined above, any suitable classificationalgorithm can be used to determine metrics and values that uniquelyidentify cellular network data that shares a same characteristic. Insome embodiments, the descriptive values and metrics will be generatedbased on two distinct data sets: 1) activation state data that isassociated with a characteristic and 2) activation state data that isnot association with a characteristic. For example: activation statedata generated from discrete cell populations after stimulation with amodulator and activation state data generated from un-stimulateddiscrete cell populations. In these embodiments, the descriptive valuesand metrics will be used to generate a two-class classifier. In otherembodiments, descriptive values and metrics will be generated from alarge number of activation state data sets associated with differentcharacteristics and a multi-class classifier will be generated. Theresulting classifier will be used to determine whether a cellularnetwork is part of the data set.

In some embodiments, the above classifiers are used to characterizeactivation state data derived from an individual such as a patient. Inthese embodiments, activation state data associated with a cellularnetwork of one or more discrete cell populations is derived from apatient. In some embodiments, the activation state data associated withthe different discrete cell populations from a patient may be identifiedby obtaining patient samples with different characteristics (e.g. bloodcells and tumor samples). In some embodiments, the activation state dataassociated with the different discrete cell populations may beidentified computationally based on activation state data foractivatable elements that are known to differentiate discrete cellpopulations. A classifier that specifies activation state data fromdifferent discrete cell populations used to determine whether the cellshave a common characteristic is applied to the activation state dataassociated with the individual in order to generate a classificationvalue that specifies the probability that the individual (or the cellsderived from the individual) is associated with the characteristic. Inmost embodiments, the classifier is stored in computer memory orcomputer-readable storage media as a set of values or executable codeand applying the classifier comprises executing code that applies theclassifier to the activation state data associated with the individual.The classification value may be output to a user, transmit to an entityrequesting the classification value and/or stored in memory associatedwith a computer. The classification value may represent informationrelated to or representing the physiological status of the individualsuch as a diagnosis, a prognosis or a predicted response to treatment.

In some embodiments, the activation state data of a plurality of cellpopulations is determined in normal individuals or individual notsuffering or not suspected of suffering from a condition. Thisactivation state data can be used to create statistical model of theranges of activation levels observed in cell populations derived fromsamples obtained from normal patients (e.g. regression model, variancemodel). This ranges and/or models may be used to determine whethersamples from undiagnosed individuals exhibit the range of activationstate data observed in normal samples (e.g., range of normal activationlevels). This can be used to create a classifier for normal individuals.In some embodiments, the models may be used to generate a similarityvalue that indicates the similarity of the activation state dataassociated with the undiagnosed individual to the range of normalactivation levels (e.g. correlation coefficient, fitting metric) and/ora probability value that indicates the probability that the activationstate data would be similar to the range of normal activation levels bychance (i.e. probability value and/or associated confidence value). Inother embodiments, activation state data from normal patients may becombined with activation state data from patients that are known to havea disease to create a binary or multi-class classifier. In someembodiments, the activation state data from an undiagnosed individualwill be displayed graphically with the range of activation statesobserved in normal cells. This allows for a person, for example aphysician, to visually assess the similarity of the activation statedata associated with the undiagnosed patient to that range of activationstates observed in samples from normal individuals.

In one embodiment, a clinical decision can be made based on a similarityvalue. In one embodiment, a clinical decision can be a diagnosis,prognosis, course of treatment, or monitoring of a subject.

In some embodiments, methods are provided for evaluating cells that maybe cancerous. The cells are subjected to the methods described hereinand compared to a population of normal cells. The comparison can be donewith any of the algorithms described herein. In some embodiments, theactivation state data is represented in graphical form. Typically, whenshown in a graph, normal cells have a uniform population and appeartightly grouped with narrow boundaries. When cancerous or pre-cancerouscells are subject to the same methods as normal cells (e.g., treatmentwith one or more modulators) and are represented on the same graph,deviations from the norm shown by the graph indicate a moreheterogeneous population. This change is an indication that the cellsmay be cancerous in a manner that is a function of the degree of change.Morphology change may indicate a cancerous population on a continuationfrom mild to metastatic. If there is no shape change from normal, thenthere may not be a change in the cell phenotype.

The presence of a heterogeneous population of cells may indicate thattherapy is needed. The outcome of the therapy can be monitored byreference to the graph. A change from a more heterogeneous population toa population that is more tightly grouped on the chart may indicate thatthe cell population is returning to a normal state. The lack of changemay indicate that the therapy is not working and the cell population isrefractory or resistant to therapy. It may also indicate that adifferent discrete cell population has changed over to the cancerousphenotype. Lack of change back to normal is indicative of a negativecorrelation to therapy. These changes may be genetic or epigenetic.

One embodiment of the present invention is to conduct the methodsdescribed herein by analyzing a population of normal cells to create apattern or a database that can be compared in a graphical way to a cellpopulation that is potentially cancerous. The analysis can be by manymethods, but one preferred method is the use of flow cytometry.

In all these embodiments, the activation state data may be generated ata central laboratory and the classifier may be applied to the data atthe central laboratory. Alternately, the activation state data may begenerate by a third party and transmitted, for example, via a securenetwork to a central laboratory for classification. Methods oftransmitting data for classification and analysis are outlined in U.S.patent application Ser. No. 12/688,851, the entirety of which isincorporated herein by reference, for all purposes.

Methods

The methods described herein are suitable for any condition for which acorrelation between the cell signaling profile of a cell and thedetermination of a disease predisposition, diagnosis, prognosis, and/orcourse of treatment in samples from individuals may be ascertained. Insome embodiments, the methods described herein are directed to methodsfor analysis, drug screening, diagnosis, prognosis, and for methods ofdisease treatment and prediction. In some embodiments, the methodsdescribed herein comprise methods of analyzing experimental data. Insome embodiments, the cell signaling profile of a cell populationcomprising a genetic alteration is used, e.g., in diagnosis or prognosisof a condition, patient selection for therapy, e.g., using some of theagents identified herein, to monitor treatment, modify therapeuticregimens, and/or to further optimize the selection of therapeutic agentswhich may be administered as one or a combination of agents. In someembodiments, the cell population is not associated and/or is notcausative of the condition. In some embodiments, the cell population isassociated with the condition but it has not yet developed thecondition. The cell signaling profile of a cell population can bedetermined by determining the activation level of at least oneactivatable element in response to at least one modulator in one or morecells belonging to the cell population. The cell signaling profile of acell population can be determined by adjusting the profile based on thepresence of unhealthy cells in a sample.

In one embodiment, the methods described herein can be used to preventdisease, e.g., cancer by identifying a predisposition to the disease forwhich a medical intervention is available. In another embodiment, anindividual afflicted with a condition can be treated. In anotherembodiment, methods are provided for assigning an individual to a riskgroup. In another embodiment, methods of predicting the increased riskof relapse of a condition are provided. In another embodiment, methodsof predicting the risk of developing secondary complications areprovided. In another embodiment, methods of choosing a therapy for anindividual are provided. In another embodiment, methods of predictingthe duration of response to a therapy are provided. In anotherembodiment, methods are provided for predicting a response to a therapy.In another embodiment, methods are provided for determining the efficacyof a therapy in an individual. In another embodiment, methods areprovided for determining the prognosis for an individual.

The cell signaling profile of a cell population can serve as aprognostic indicator of the course of a condition, e.g. whether a personwill develop a certain tumor or other pathologic conditions, whether thecourse of a neoplastic or a hematopoietic condition in an individualwill be aggressive or indolent. The prognostic indicator can aid ahealthcare provider, e.g., a clinician, in managing healthcare for theperson and in evaluating one or more modalities of treatment that can beused. In another embodiment, the methods provided herein provideinformation to a healthcare provider, e.g., a physician, to aid in theclinical management of a person so that the information may betranslated into action, including treatment, prognosis or prediction.

In some embodiments, the methods described herein are used to screencandidate compounds useful in the treatment of a condition or toidentify new druggable targets.

In another embodiment, the cell signaling profile of a cell populationcan be used to confirm or refute a diagnosis of a pre-pathological orpathological condition.

In instances where an individual has a known pre-pathologic orpathologic condition, the cell signaling profile of the cell populationcan be used to predict the response of the individual to availabletreatment options. In one embodiment, an individual treated with theintent to reduce in number or ablate cells that are causative orassociated with a pre-pathological or pathological condition can bemonitored to assess the decrease in such cells and the state of acellular network over time. A reduction in causative or associated cellsmay or may not be associated with the disappearance or lessening ofdisease symptoms. If the anticipated decrease in cell number and/orimprovement in the state of a cellular network do not occur, furthertreatment with the same or a different treatment regiment may bewarranted.

In another embodiment, an individual treated to reverse or arrest theprogression of a pre-pathological condition can be monitored to assessthe reversion rate or percentage of cells arrested at thepre-pathological status point. If the anticipated reversion rate is notseen or cells do not arrest at the desired pre-pathological status pointfurther treatment with the same or a different treatment regime can beconsidered.

In a further embodiment, cells of an individual can be analyzed to seeif treatment with a differentiating agent has pushed a cell type along aspecific tissue lineage and to terminally differentiate with subsequentloss of proliferative or renewal capacity. Such treatment may be usedpreventively to keep the number of dedifferentiated cells associatedwith disease at a low level, thereby preventing the development of overtdisease. Alternatively, such treatment may be used in regenerativemedicine to coax or direct pluripotent or multipotent stem cells down adesired tissue or organ specific lineage and thereby accelerate orimprove the healing process.

Individuals may also be monitored for the appearance or increase in cellnumber of another cell population(s) that are associated with a goodprognosis. If a beneficial population of cells is observed, measures canbe taken to further increase their numbers, such as the administrationof growth factors. Alternatively, individuals may be monitored for theappearance or increase in cell number of another cells population(s)associated with a poor prognosis. In such a situation, renewed therapycan be considered including continuing, modifying the present therapy orinitiating another type of therapy.

Reports and Computers

In some embodiments, a report can be generated that can be used tocommunicate a signaling pathway activity in single cells, identifysignaling pathway disruptions in diseased cells, including rare cellpopulations, identify response and resistant biological profiles thatguide the selection of therapeutic regimens, monitor the effects oftherapeutic treatments on signaling in diseased cells, and/or monitorthe effects of treatment over time. A report can enable biology-drivenpatient management and drug development, improve patient outcome, reduceinefficient uses of resources, and improve speed of drug developmentcycles.

A report can compare a signaling profile from one or more normal cellsto a signaling profile from a test subject, e.g., a patient, e.g., anundiagnosed individual. A report can compare an activation level of oneor more activable elements from one or more normal cells to anactivation level of the one or more activable elements from a cell froma test subject, e.g., a patient, e.g., an undiagnosed individual.

Examples of a report are shown in FIGS. 8, 9, and 10. A report canprovide information on the types of cells in a patient sample (see e.g.,FIGS. 8, 9, and 10). A report can comprise information on a percentageof a type of a cell in a patient sample (see, e.g., FIGS. 8, 9, and 10).A report can provide information on the percentage range of a type ofcell in a normal or healthy sample. The type of cell can be determinedbased on the surface phenotype of the cell, and the surface phenotype ofthe cell can be included in the report. The range of percentage ofnormal or healthy cells in a sample can be compared to the percentage ofa type of cell from a patient on a linear graph (see e.g., FIGS. 8 and10) or a circular diagram (see e.g., FIG. 9A).

A report can provide information on a signaling phenotype. Signalinginformation can be presented as a radar plot (see e.g., FIGS. 8 and 10).A radar plot can also be known as a web chart, spider chart, star chart,star plot, cobweb chart, irregular polygon, polar chart, or kiviatdiagram. Information on a report can include a comparison of signalinginformation from a patient (a test sample) to signaling information fromnormal or healthy samples. Information on normal samples can compriseinformation on the range of activation levels of activatable elements.The range can be indicated by a color, e.g., gray, on a radar plot. Therange of activation levels can be expressed as fold changes inactivation levels for activatable elements when cells are in thepresence of a modulator relative to when cells are in the absence of themodulator. Other metrics can be used to compare patient samples tovalues for normal or healthy cells. The information on the activationlevels of activatable elements from a patient (e.g., fold change whencells are in the presence of a modulator relative to cells in theabsence of a modulator) can be plotted on the radar plot to allow acomparison of signaling data between the patient sample and the normalor healthy samples. Data on the patient sample can be represented in adifferent color than data for the normal or healthy samples, anddifferent colors can be used for different cell subpopulations. A radarplot can include information on a modulator used in an experiment (e.g.,TPO, SCF, FLT3L, G-CSF, IL-3) and on an activatable element (e.g.,p-STAT3, p-ERK, p-AKT, p-S6, p-AKT, p-STAT1). The report can containinformation regarding whether samples were treated or not treated with akinase inhibitor. A report can illustrate cell lineage information (seee.g., FIG. 8).

Cell signaling information can also be represented as a heat map (seee.g., FIGS. 9B and 9C). The activation level of an activatable elementrelative to a basal state can be represented by a color scale. The colorscale can comprise shades of yellow and blue or shades of red and green,for example.

A report can include information on cell growth (see e.g., FIGS. 9D and10H). The information on cell growth can include information on one ormore treatments, percentage of non-apoptotic cells, percentage of S/G2phase cells, and percentage of M phase cells. The information on cellgrowth can compare cell growth of a patient sample to a normal orhealthy control. The information on cell growth can include informationon growth factor dependent effects on cell growth and/or survival.

A report can include information on the effects of a drug on a cell,e.g., cell survival and/or cytostasis (see e.g., FIGS. 9D, 9E, 10I, 10J,and 10K). Information on percent survival can be plotted as a radarplot, e.g., a survival radar plot (see e.g., FIG. 10I). The informationon cell survival and/or cytostasis can include drug target and drugsthat are tested. The percentage of non-apoptotic cells can be normalizedto an untreated control (untreated can equal 100%). A color (e.g., gray)can show a range of response from a healthy sample, e.g., a healthy bonemarrow sample. In the example shown in FIGS. 10J and 10K, for patient#1910-017, myeloid cells resisted apoptosis for most drugs, includingAraC. However, two drugs were effective at inducing apoptosis:bortezomib (a proteosome inhibitor) and NVP-AuY922 (an HSP90 inhibitor).

Information on cell survival and/or cytostasis after drug exposure caninclude a cytostasis radar plot (see e.g., FIGS. 10J and 10K). Asanother example of information that can be included in a report, samplescan be gated on non-apoptotic cell populations and that information canbe displayed. A cytostasis radar plot can indicate cell-cycleinformation, e.g., a percentage of cells in M-phase or a percentage ofcells in S/G2 phase normalized to an untreated control (e.g., anuntreated control can equal 100%). In the examples shown in FIG. 10,although most drugs tested on patient sample #1910-017 have a mildeffect on cell survival, many drugs can prevent cell growth(cytostasis). Information on apoptosis and cytostasis can be plotted asshown in FIGS. 9D and 9E. The results of other cell tests can beincluded in a report, such as those shown in U.S. Patent Publication No.20100204973.

Direct graphical comparison between a range of activation level of anactivatable element for normal or healthy cells compared to theactivation level of the activation element for cells in a test sample(e.g., diseased cells) can identify aberrant signaling processes and/orsurvival mechanisms that can inform strategies for targeting a subjectfrom whom the test sample was taken with a therapeutic. For example,aberrantly high thrombopoietin (TPO) signaling can reveal a dependenceon TPO receptor signaling for optimal tumor cell survival and/orproliferation. Thus, targeting TPO signaling with one or more moleculesthat can attenuate the signal (e.g., kinase inhibitors, neutralizingantibodies, etc.) can slow tumor growth.

In some embodiments, a report can comprise information regarding, e.g.,patient or subject indentifying information (e.g., name, age, gender,date of birth, weight, eye color, and/or hair color), insuranceinformation, healthcare provider information (e.g., physician name,address of business, type of practice, etc.), medical history, bloodpressure information, pulse rate information, information ontherapeutics the subject is taking (e.g., name of therapeutic, dose,administration schedule, etc.), billing information, sampleidentification information, and/or order number. A report can comprise asummary, a diagnosis, a prognosis, or a therapeutic suggestion. Atherapeutic suggestion can comprise a type of drug, a dose of drug, or adrug administration schedule. A report can comprise a barcode toidentify the report or link the report to a subject. A report cancomprise information on a clinical trial.

In some embodiments, a method is provided for determining an activationlevel of one or more activatable elements in normal cells and/or cellsfrom a test subject (e.g., an undiagnosed subject), wherein the normalcells and/or cells from the test subject (e.g., an undiagnosed subject)are, or are not, contacted with a modulator, and transmitting data onthe activation level of the one or more activatable elements to acentral server for analysis and report generation. In one embodiment, aserver communication module can receive a report from a centrallaboratory server. A report can comprise, e.g., a hyperlinked document,a graphic user interface, executable code, and/or physical document. Areport can be accessed via a secure web portal. A server communicationmodule can display a report to a third party and allow a third party tointeractively browse a report. In some embodiments, a servercommunication module allows a third party to specify a format they wouldlike to receive a report in or specific types of data (e.g., pathwaysdata, clinical trials data, partner biometric data) they would like toinclude in a report. In an instance where a received report isassociated with a patient sample, a server communication module canre-integrate patient information that has been scrubbed from clinicaldata in a report.

In one embodiment, a report generation module generates interactivereports which a third party can navigate to view report information.Reports can be displayed in a web browser or module software. A reportgeneration module can generate a static report, e.g., a hard copydocument.

A report generation module can function to generate a report for a thirdparty based on the activation level of one or more activatable elementsand an association metric. A report generation module can combine theactivation level of one or more activatable elements and an associationmetric for a sample with additional information from publicbioinformatics databases and partner a biometric information database togenerate a report. A report generation module can retrieve dataassociated with a biological state from an external source such as apublic bioinformatics database and combine this data with data on theactivation state of an activatable element and an association metric togenerate a report. In some embodiments, a report generation module canperiodically retrieve this data and store the data in association with astatistical model in a biological state model dataset. A reportgeneration module can retrieve clinical information associated with asample from a partner biometric information database. A reportgeneration module can also retrieve the activation level of one or moreactivation elements associated with a prior report for a client from anactivation level database.

A report generation module can communicate with an activation levelmetric module, and a model generation module can generate graphicalsummaries of activation level data. Graphical summaries of data caninclude, e.g., bar plots of activation level data, gated plots ofactivation level data, line plots of activation level data, and pathwayvisualizations of activation level data. A report generation module canfurther communicate with an association metric module to produce atextual summary of association metric data. A textual summary caninclude a diagnostic of a disease state in a patient, recommendedtreatment regimen for a patient, a grade disease-subtype of a patient ora prognosis for a patient. A report generation module can incorporategraphical and textual summaries of activation level data into a report.

In some embodiments, a report generation module can then transmit agenerated report to a third party client via a communication module ordisplay a generated report to a third party client via a secure webportal. In other embodiments, a report generation module can physicallytransmit a report to a third party as a hard copy paper document or asexecutable code encoded on a computer-readable storage medium.

A report can be provided to a subject (e.g., a subject from whom a testsample was taken). A report can be provided to an insurance company. Areport can be provided to a healthcare provider (e.g., physician,surgeon, nurse, first responder, dentist, psychiatrist, psychologist,anesthesiologist, etc.).

Sample Grouping or Characteristic

In some embodiments, samples from a test subject, e.g., an undiagnosedindividual (e.g., samples comprising undiagnosed cells) and normalindividual (normal cells) can be compared based on a sample grouping orcharacteristic, e.g., age, race, gender, ethnicity, physicalcharacteristic, socioeconomic status, income, occupation, geographiclocation of birth, education level, diet, exercise level, etc.

A sample grouping or characteristic can be age. The age of an individual(e.g., test subject or, normal subject) from whom a sample can bederived can be about, more than about, or less than about 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46. 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,112, 113, 114, 115, 116, 117, 118, 119 or 120 years old. The testsubject (e.g., undiagnosed individual) or normal subject can be, e.g., afetus, a newborn, an infant, a child, a teenager, an adult, or anelderly person. An activation level of one or more activatable elementsin an a sample from a test subject (e.g., an undiagnosed sample; samplefrom an undiagnosed individual) can be compared to an activation levelof the one or more activatable elements from normal samples derived fromnormal subjects that are, e.g., about 1-5, 5-10, 1-10, 10-15, 10-20,15-20, 20-25, 20-30, 25-30, 30-35, 35-40, 40-45, 40-50, 45-50, 50-55,50-60, 55-60, 60-65, 60-70, 65-70, 70-75, 75-80, 70-80, 80-85, 80-90,85-90, 90-95, 90-100, 95-100, 100-105, 100-110, 105-110, 110-115,110-120, 115-120, 1-20, 20-40, 40-60, 60-80, 80-100, or 100-120 yearsold. A test subject can be of an age that falls into any one of theaforementioned ranges. A test subject and/or normal subject can beabout, more than about, or less than about 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, or 12 months old. Normal subjects can be selected for analysisbased on the age of the normal subjects.

A sample grouping or characteristic can be race, ethnicity, birthcountry, and/or geographic location. A sample grouping or characteristicof a test subject and/or normal subject can be, e.g., a EuropeanAmerican, an African-American, Caucasian, Asian, Hispanic, or Latino. Inanother embodiment, a sample grouping or characteristic of a testsubject and/or normal subject can be, e.g., Abzinz, Abenaki, Abipones,Abkhazs, Aborigines, Abron, Acadian, Accohannock, Achang, Acelmese,Acholi, Achomawi, Acoma, Adi, Adjarians, Adyghe, Adyhaffe, Aeta, Afar,African-American, African Canadian, African Hebrew Israelites ofJerusalem, Afrikaners, Afro-American peoples of the Americas (e.g., AfroArgentine, Afro Bolivian, Afro Brazilian, Afro-Chilean, Afro-Colombian,Afro-Costa Rican, Afro-Cuban, Afro-Dominican, Afro-Ecuadorian people,Afro-Guyanese, Afro-Latino, Afro-Jamaican, Afro-Mexican, Afro-Peruvian,Afro-Portuguese, Afro-Puerto Rican, Afro-Trimidadian, Afro-Uruguayan),Aftsarians or Isaurians, Agaw, Agni, Aguls, Ahtna, Aimaq, Ainu, Aynu ofChina, Aja, Aka, Akie, Ak Chin, Akan, Akha, Akuapem, Akhvakh people,Akyem, Alabama, Alak, Albanians, Albanian American, Albanian Australian,Aleut, Algonquian, Aliutors, Alsatians, Amahuaca, Amerasians,Americo-Liberians, Amhara, Amish, Amungme, Andalusians, Andis, Anga,Anglo-African, Anglo-Celtic Australian, Anglo-Indian, Anglo-Saxon,Annamites or Vietnamese or Kinh or Jing, Ansar people or Ansarie, Anuak,Apaches, Apinaje, Arab (e.g., Palestinian diaspora, Afro-Arab, ArabAmerican, Arab Argentine, Arab Australian, Arab Brazilian, Arab Britons,Arabs in Bulgaria, Arab Canadian, Arab Chilean, Arab Ecuadorians, ArabHaitian, Arabs in France, Arabs in Germany, Arabs in Greece, Arabs inPalestine, Arabs in Italy, Arab Jews, Arab Mexican, Arabs in theNetherlands, Arabs in Pakistan, Arab Peruvian, Arab Singaporean, ArabSri Lankans, Arabs in Sweden, Arabs in Turkey, Arab Venezuelan, Arabdiaspora in Colombia, Arabs in Afghanistan, Iranian Arabs), Aramaic,Araon, Aragonese, Arapaho, Arawak, Arbëreshë, Archis, Arikara,Armenians, Armenian American, Aromanians (or Macedo-Romanians),Arvanites, Atoni, Aryans/Indo-Iranians, Indo-Aryan peoples, Iranianpeoples, Nuristani people, Asante, Asheninka, Asmat, Assiniboine,Assyrians, Asturians, Atacameno, Atta, Ati, Atikamekw, Atsina, Atsugewi,Aukstaitians, Australian aborigine, Austrians, Avars, Awá, Aymaras,Ayta, Ayrums, Azeris, Aztecs, Ayapaneco, Babongo, Bahrani people, Badui,Baggara or Baqqarah, Baguirmi, Bagulals, Bai, Bajau, Baka, Bakhtiyari,Balinese, Bakongo/Kongo, Balkars, Baloch (also Baluch, Balochi), BalticGermans, Bamar (also Burmese and Burman), Bambara, Bamileke, BanatSwabians, Banawa, Banda, Bandjabi, Banjar, Bantu, Baoule, Bapou, Bariba,Bartangs, Basarwa, Bashkirs, Basotho, Basques, Basque Argentine, BasqueAmerican, Basque Chilean, Bassa, Bassari, Baster (also known asBaaster), Batak, Batak, Bateke, Bats, Batswana (also Tswana), Bavarians,Beaver, Bedouins, Beja, Belarusians, Bengalis, Bengali American, BengaliHindus, Bemba, Bene Israel, Berbers, Berom, Betamaribe, Bethio,Beti-Pahuin, Bezhtas, Bhotia, Bhotiya, Bicolano, Biharis, Blackfeet (orBlackfoot), Black British, Black Canadians, Black Indians, Bo Y, Bodo,Boere-Afrikaners, Bonairean, Bonan, Borinquen, Bosniaks, Bostonian,Botlikhs, Bouganvilleans, Boyar, Boyko, Bozo, Brau, Brazilian, Bretons,British, British American, British Canadian, British Chileans, Brulé,Bru-Van Kieu, Bubi, Budukhs, Bugis, Bulang, Bulgarians, Bulgars,Bunjevci, Burgenland Croats, Buryats, Bushongo, Buyi, Caddo, Cahuilla,Caingang, Cajun, Caldoche, Californio, Cambodia, Campa, Canadians,Canarians, Cantonese, Cape Coloured, Cape Malay, Castilians, Caprivian,Caribs, Carinthian Slovenes, Caripuna, Catalans, Catawba, Cayuga,Cayuse, Cebuano, Celts, Ceylon Moors, Chagga, Cham, Chambri, Chamalals,Chamorro, Charrúa, Chechens, Chehalis, Chemehuevi, Chepang, Chere,Cherokee, Cheyenne, Chicanos, Chickahominy, Chickasaw, Chilcotin,Chileans, Chilean American, Chilean Australian, Chilean Swedes,Chimakum, Chinese (also known as Han or Han Chinese), Chinese American,Chinese Australian, Chinese Brazilian, Chinese Canadian, BritishChinese, Ethnic Chinese in Brunei, Chinese people in Bulgaria, BurmeseChinese, Chinese Cambodian, Chinese Canadian, Chinese people in Chile,Chinese-Costa Rican, Chinese Cuban, Chinese in Fiji, Chinese Filipino,Chinese diaspora in France, Chinese Indonesian, Chinese people in Italy,Chinese Jamaican, Chinese people in Japan, Ethnic Chinese in Korea,Laotian Chinese, Malaysian Chinese, Chinese Mauritian, Chinese Mexican,Ethnic Chinese in Mongolia, Chinese New Zealander, Chinese Nicaraguan,Ethnic Chinese in Panama, Chinese Peruvian, Chinese of Romania, EthnicChinese in Russia, Chinese in Samoa, Chinese Singaporean, Chinese SouthAfricans, Chinese people in Spain, That Chinese, Chinese in Tonga,Chinese Trimidadian, Chinese Vietnamese, Chinookan, Chipewyan, Chippewa,Chitimacha, Cho Ro, Choctaw, Chukchansi, Chukchis, Chulym Tatars,Chumash, Chuncho, Chut, Chuukese, Chuvash, Ciboney, Circassians orCherkezians, Clayoquot, Co people, Coalhuiltec, Co Ho people, Co Lao, CoTu people, Coast Salish, Cochiti, Cocopah, Coeur d'Alene, Coharie,Colchians or Kolchians, Colombians, Coloured, Colville, Comanche,Comorian, Cong, Congolese people, Copper, Coquille, Corsicans, Cornish,Cornish American, Cornish Australian, Cossack, Costanoan, Coushatta,Cowichan, Cowlitz, Cree, Creek, Créole, Crimean Germans, Crimean Goths,Crimean Tatars, Croats, Croatian American, Croatian Australian, CroatianBrazilian, Croatian Canadians, Croatian Chileans and Croatian-Peruvians,Crow, Cubans, Cuban Americans, Cumans, Cupeño, Curaçaoan, GreekCypriots, Czechs, Czech American, Czechs in the United Kingdom, CzechCanadian, Daasanach, Dadhich, Dai (That, That Lue), Dakelh, Dakota,Damara, Danish, Danish American, Danish Australian, Danish Canadian,Danmin, Darhad, Dargins, Daribi, Daur, Dayaks, De'ang, Deg Hit'an, Degar(Montagnards), Delaware, Dena'in a (also known as the Tanaina), Dendi,Derbish, Desana, Dhivehis, Dhodia, Didos, also known as Tsez, Diegueno,Dinka, Diola, Dogon, Dolgans, Dom, Doma, Dominicans, Dominican American,Don Cossacks, Dong, Dongxiang, Dorze, Dorians, Dravidians, Drung, Druze,Du people, Duala people, Dungan, Dutch, Dutch American, DutchAustralian, Dutch Brazilian, Dutch Canadian, Cape Dutch, Dutch NewZealander, Dyula (Jula), Ebira, Ecuadorian, Egyptians, Elema, Enets,Enga, English, English American, English Australian, English Canadian,English Brazilian, English African, English Argentine, Anglo-Burmese,Anglo-Indian, Enxet, Eshira, Eskimo, Esselen, Estonians, EuropeanAmericans, Evens, Evenki, Ewe, Expatriata Americana, Falasha/BetaIsrael, Fante, Faroese, Fars, Fereydan, Fernandinos, Fijian, Fir Bolg,Finns, Finnish American, Flemish, West Flemings, Fon, Fox,Franco-Mauritian, Franco-Réunionnaise, Franks, Franconians, French,French American, French British, French Mexican, French Argentine,French Chilean, French Canadian, Frisians, Fula (also called Fulani orFulbe), Fulni-o, Fur, Ga, Gaels, Gagauz, Galicians, Gaoshan,Garifuna/Garinagu, Garo, Gbaya people, Ge, Geba Buru, Gelao, Georgian,Georgian American, Germans, German American, German Argentine, GermanAustralian, German Brazilian, German-Briton, Germans in Bulgaria, GermanCanadian, German-Chilean, Germans in the Czech Republic, Germans ofHungary, Germans of Kazakhstan, German Mexican, German Peruvian, Germansin Poland, Germans in Romania, Germans from Slovakia, Germans ofYugoslavia, Gia Rai, Giay, Gie Trieng, Gitanos, Godoberis, Gogodali,Gongduk, Gorals, Gorani, Goshute, Gotlanders, Goulaye, Greeks, Griqua,Gros Ventre, Gruzinim, Guadeloupean, Guajajara, Guarani, Gujaratis,Gullah, Gurage, Guria, Guru, Guruks, Gurung, Hadza, Haida, HaitianCreole, Hakka, Haliwa-Saponi, Hamer, Hamshenis, Han Chinese, Hani,Hausa, Havasupai, Haw, Hawaiian, Hapas, Hazara, Herero, Hesquiat,Hezhen, Hidatsa, Himba, Hindoestanen, Hindi people, Hinukhs, Hispanics,Hmar, Hmong, Hoa, Ho-Chunk, Hoh, Hohokam, Hoklo, Holikachuk, Hopi,Houma, H'Re, Hualapai, Huastec, Hui Chinese, Huicol, Hungarians, Huns,Hunzakuts, Huli, Hunzibs, Hupa, Hurrians, Huron, Hutsuls, Hutu, Iatmul,Iban, Ibanag, Ibibio, Icelanders, Icelandic American, IcelandicCanadian, Igbo, Igbo American, Igbo Jamaican, Igbo Canadian, Igorot,I-Kiribati, Illiniwek, Ilocano, Ilonggo, Imereti, Incan, Indo-Aryan,Indo-Caribbean, Indo-Europeans, Indo-Guyanese, Indo-Iranians, Indo-Aryanpeoples, Iranian peoples, Nuristani people, Indo-Trimidadian, Ingessana,Ingrians, Ingushes, Innu, Inuit, Irani, Iranian, Irish, Irish American,Irish Argentine, Irish Australian, Irish British, Irish Canadians, IrishChilean, Irish Puerto Rican, Irish Mexican, Irish Newfoundlanders, IrishTraveller, Irish Quebecers, Iroquois, Ishkashmis, Isleta, Isoko,Istriot, Istro-Romanians, Italians, Italian American, Italian Argentine,Italian Australian, Italian Brazilian, Italians in the United Kingdom,Italian Canadian, Italian-Chilean, Italian Egyptian, Italians inGermany, Italian Jews, Italian settlers in Libya, Italian Mexican,Italian Peruvian, Italians of Romania, Italian Scots, Italian Swiss,Tunisian Italians, Italian settlement in Uruguay, Italo-Venezuelans,Welsh Italians, Itelmens, Itsekiri, Izhorians, Jakaltek people, Jakut,Jamaican, Janjevci, Japanese, ethnic Japanese, Japanese Americans,Japanese Australians, Japanese Brazilians, Japanese Canadians, JapaneseChileans, Japanese settlement in the Philippines, Japanese people inFrance, Japanese people in Germany, Japanese in Hawaii, JapaneseMexicans, Japanese in the United Kingdom, Japanese Peruvians, Jassic(Jász), Jat, Javanese, Jebala, Jemez, Jewish, Jing, Jingpo, Jino,Jivaro, Jola, Jopadhola, Jri, Jutes, Kandahar and Kabuli, K'iche'(Quiché), Kabardin, Kabyle, Kadiweu, Kaibartta, Kakheti, Kalasha ofChitral, Kalenjin, Kallawaya, Kaliai, Kalispel, Kabuli, Kamas, Kamayura,Kannadiga, Kanembu, Kapauku, Kapampangan, Karachay, Karaims, Karajá,Karakalpaks, Karamanlides, Karamojong, Karatas, Karelians, Karen, Karok,Kashubians, Katang, Kato, Katuquina, Kavango, Kaw or Kansa Indians,Kayapo, Kazakhs, Kenyah, Kenyan American, Kereks, Keresan, Kets, Khakas,Khang, Khants, Khasia, Khassonké, Khevi, Khevsureti, Khinalugs, Khmer,Khmer American, Khmu, Kho Mu, Khoikhoi, Khojas, Khomani or Nu, Khufis,Khvarchis, Kickapoo, Kikuyu, Kinh or Jing or Vietnamese, Kiowa, Klallam,Klamath, Klikitat, Kolchan, Kombai, Kogi, Komi, Koniag, Kongo, Kootenai,Koptian, Korean, Korean American, Koreans in Argentina, KoreanAustralian, Korean Brazilian, Koreans in the United Kingdom, KoreanCanadian, Koreans in Chile, Koreans in China, Koreans in thePhilippines, Koreans in France, Koreans in Germany, Koreans inGuatemala, Koreans in Hong Kong, Koreans in the Arab world, Koreans inIndonesia, Koreans in Iran, Koreans in Japan, Koreans in Malaysia,Korean Mexican, Koreans in Micronesia, Korean New Zealander, Koreans inParaguay, Koreans in Peru, Koreans in Poland, Koreans in Singapore,Koreans in Taiwan, Koreans in Uruguay, Koreans in Vietnam, Koreanadoptees, Korowai, Koryaks, Kosraean, Koskimo, Koyukon, Kpelle, Kraho,Krashovans, Kri, Krymchaks, Kryz, Kuban Cossacks, Kubu, Kuikuru, Kuna,Kumeyaay, Kumyks, Kurds, Kuruba Gowda, Ktunaxa, Kwakiutl, Kwakwaka'wakw,Kyrgyz, La Chi, La Ha, La Hu, Laguna, Lahu, Laigain, Lakota, Laks,Lamet, Langi (also Lango), Lao, Lao American, Lao Sung, Lao Theung,Latgalians, Latvians, Lavae, Laven, Layap, Laz, Lazoi, Lebanese people,Lebanese American, Lebanese Australian, Lebanese Brazilian, Lebou,Lemkos, Lenca, Lengua, Leonese, Lezgis, Lhoba, Lhotshampa, Li,Liechtenstein, Limbus, Lipka Tatars, Lipovans, Lisu, Lithuanians,Livonians, Lo Lo, Lobi, Lotuko, Louisiana Creole people, Lozi, Lua,Luba, Lue, Luhya, Luiseno, Lumad, Lumbee, Lummi, Lunda, Luo (alsoJoluo), Lusitanians, Luso-Brazilians, Luso-American, Luxembourgers,Luxembourg American, Maasai, Macao, Macedonians, Macuxi, Madeirans,Madurese, Magar people, Magyars, Magyar American/Hungarian American,Magyar Canadian/Hungarian Canadian, Magyar Vojvodinian/Hungarians inVojvodina, Mahican, Mahorian, Maidu, Mailu, Maingtha, Maka, Makah,Makong, Makua, Malagasi, Malay, Malayalee, Maliseet, Maltese, Mam,Mamamwa, Manasi, Manchu, Mandan, Mandinka, Mang people, Mangbetu,Mangyan, Mansis, Manx, Maonan, Māori, Mapuche, Maratha, Marathis, Mari,Maricopa, Marind-Anim, Mashantucket Pequots, Matabele, Mataco, Matis,Mattaponi, Maubere, Maya, Mayo, Mazandarenis, M'Baka, Mbaya, Mbochi,Mbuti, Megleno-Romanians, Meherrin, Mekeo, Melungeons, Memon, Menba,Mende, Menominee, Mennonites, Amish or the Pennsylvania Dutch,Hutterites, Mentawai, Meskhetians, Mestizo, Métis, Meitei, Me-Wuk,Mbuti, Miccosukee, Mi'kmaq, Mina, Mekeo, Mexican people,Minahasa/Manadonese, Minangkabau, Mingo, Mingrelians, Miskito, Mission,Mitsogo, Miwok, Mixtec, Mizo, Mlabri, Mnong, Modoc, Mohajir, Mohave,Mohawk, Mohegan, Molise Croats, Mon, Monacan, Mongo, Mongols, Mono,Montagnais, Montaukett, Montenegrins, Moor, Moravians, Moriori, Morisco,Morlachs, Mormons, Moro people, Mossi, Motuan, Muckleshoot Indians,Mudéjar, Muhajir (Pakistan), Mulam, Mulatto, Mundas, Mundurucu, Muong,Mursi, Museu, Myene, Naga, Nahanni, Nahua, Namaqua, Nanais, Nansemond,Narragansett, Nasia, Natchez, Nauruan, Navajo, Naxi, Ndau, Ndebele,Negidals, Negrito, Nenets, Nespelem (Nespelim or Nespilim), Nevisian,Newar, Nez Percé, Ngac'ang, Ngasan, Ngae, Nganasans, Nhahuen, Nhuon,Niominka, Nipmuc, Nishka, Nisqually, Nisei, Nisse, Nivkh, Niuean,Ni-Vanuatu, Njem, Nogais, Nomlaki, Nooksack, Norwegians, NorwegianAmerican, Norwegian Canadian, Nu, N/u or Khomani, Nuba, Nubians, Nuer,Nukak, Nung, Nuristani, Nuu-chah-nulth, Nyagatom, Nzema, O Du, Odawa,Ogaden, Oglala, Ogoni, Ojibwa, Okamba, Okande, Okinawans, Omaha, Omagua,Oneida, Onondaga, O'Odham, Oroch, Orokaiva, Oroks, Oromo, Orogen,Oroshoris, Osage Nation of Oklahoma (or of Missouri, Kansas, andArkansas), Ossetians, Otavalerio, Otoe-Missouria, Ottawa, Ovambo, PaThen, Paiute, Pākehā, Pakoh, Palcene, Paliyan, Pamunkey, Pangasinanpeople, Panoan, Pa-O, Pashu, Pashtun (Pathan), Parsi, Passamaquoddy,Pataxo, Pattar, Pa-Thi, Paugusset, Pawnee, Pennsylvania Dutch, Penan,Pennsylvania German, Penobscot, Peoria, Perce, Persians, Petchenegs,Phoenicians, Phong, Phu La, Phu Noi, Phu That, Picts, Pied-noir, Piegan,Pima, Pit River Indians, Pitcairn-Norfolk, Pilaga, Polabian Slays,Polish, Polish American, Polish Australian, Polish Argentine, PolishBrazilian, Poles in the United Kingdom, Polish Canadian, Poles inGermany, Polish minority in Ireland, Polish minority in Russia, Poles inBelarus, Poles in Czechoslovakia, Poles in Ireland, Poles in Latvia,Poles in Lithuania, Poles in Romania, Poles in the former Soviet Union,Poles in the Soviet Union, Poles in Ukraine, Polynesians, Pomaks, Porno,Ponca, Ponhpeian, Pontic Greeks, Poospatuck, Portuguese, PortugueseBrazilian, Portuguese American, Portuguese Canadian, Potawatomi,Potiguara, Powhatan, Proto-Indo-Europeans, Pu Peo, Pueblo people,Puelche, Puerto Ricans, Puerto Ricans in the United States, Puget SoundSalish, Purépecha, Punan, Pumi, Punjabis, Puyallup, Qashqai, Q'eros,Qiang, Quahatika, Quapaw, Quechan, Québécois, Quechuas, Quiché(K'iche'), Quileute, Quinault, Quinqui, Ra Glai, Rais, Rakhine, Rakuba,Ramapough Mountain Indians, Rappahannock, Rashaida, Ro Mam, Rohingya,Roma, Romanians, Romanian American, Roshanis, Rotuman, Russians, RussianAmerican, Russian Australian, Russians in Belarus, Russians in Bulgaria,Russian Brazilian, Russian Canadian, Russians in China, Russians inFinland, Russians in Georgia, Russians in Japan, Russians in Kazakhstan,Russians in Korea, Russians in Ukraine, Russians in Mexico, Russians inGermany, Rusyns, Ruthenians, Rutuls, Ryukyuans, Sadang, Saek, Saho,Saingolo, Salar, Salish, Samanthan, Samaritan, Samegrelo, Sami, Samoans,Samogitians, Samojeeds, Samtao, Samburu, San, San Chay, San Diu, Sanema,Santal, Santee Sioux, Saponi, Sara, Saramaka, Sarakatsani, Sardinians,Sauk, Sauk-Suiattle, Saxons, Scottish-American, Scots-Irish, orScotch-Irish, Scots-Irish American, Scottish, Sekani, Selk'nam, Selkies,Selkups, Semai, Seminole, Sena, Seneca, Senegalese people, Sentinelese,Serbs, Serer, Serer-Ndut, Seychellois Creole people, Seychellois people,Shan, Shangaan, Shasta, Shavante, Shawnee, She, Sherpa, Shinnecock,Shipibo, Shoalwater Bay Tribe, Shona, Shors, Shoshone, Shughnis, Shui,Si La, Sicilians, Sicilian American, Sidamo, Siddi, Siksika, Silesians,Siletz, Sindhis, Singmun, Sinhalese or Sinhalas, Sinti, Sioux, Siuslaw,Skagit, S'Klallam, Skokomish, Skwxwú7mesh, Slays, Slovaks, SlovakAmerican, Slovaks in Bulgaria, Slovaks in Vojvodina, Slovenes, SloveneAmericans, Slovene Australians, Slovene Canadians, Slovene Hungarians,Sokci, Somali, Somba, Songhai, Soninke, Sorbs, Souei, (South African),Southern Tutchone, Southerners or Southern Americans, Spanish, SpanishAmerican, Spokane, Squaxin Island Tribe, Sri Lankan Moors,Stillaguamish, Sundanese, Sudanese people, Sudanese American, SudaneseAustralian, Suquamish, Suri, Surui, Susu, Suyá, Svans, Aramean-Syriacs,Swahili people, Swazi, Swedes, Swedish American, Swedish Argentine,Swedish Australian, Swedish British, Swedish Canadian, Swedish Estonian,Swedish Finns, Swinomish, Swiss, Swiss German, Swiss French, SwissItalian, Swiss Romansh, T'boli, Ta Oi, Tabasarans, Tache, Tachi,Tagalogs, Tagish, Taíno, Taiwan, Taiwanese American, Taiwaneseaborigines, Tajik, Tajiks in China, Taliang, Talysh, Tamang, Tamil,Tamil British, Tamil Canadian, Tamil Indians in Sri Lanka, TamilMalaysians, Tamil Sri Lankans, Tanna, Tanana, Taos, Tapajo, Tapirapé,Tapuia, Tarahumara, Tarascan, Tasaday, Tatars, Tats, Tay, Teda,Tehuelche, Teimani Jewish, Tejano, Telefolmin, Terena, Tetons, Tewa,Texans, That, That American, That Australian, That British, Thakali,Tharu, Thin, Th{circumflex over ({dot over (o)}, Tibetans, Ticuna,Tigray people, Tigray-Tigrinia, Tigre people, Tigrinya people, Tigua,Tindis, Tipra, Tlakluit, Tlingit, Toala, Toba, Tofalars, Tohono O'odham,Tokelauan, Tolowa, Tolais, Toltec, Tonga, Tongans, Tongva, Tonkawa,Topachula, Toraja, Torbesh, Torres Strait Islanders, Totonac, Toubou,Transylvanian Saxons, Trukhmens, Tsakhurs, Tsetsaut, Tsez, Tsimishian,Tsonga, Tsuu T'ina, Tswana people, Tuareg, Tujia, Tukano, Tukolor,Tuamotu, Tulalip, Tulutni, Turn, Tumbuka, Tungus, Tunica-Biloxi, Tupian,Tupinamba, Turkmen, Turks, Turkish American, Turkish Australian, Turksin Austria, Turks in Azerbaijan, Turks in Belgium, Turkish British,Turkish Canadian, Turkish Cypriots, Turks in Denmark, Turks in France,Turkish Germans, Turks in Japan, Turks in Liechtenstein, Turks in theNetherlands, Turks in Norway, Turks in Sweden, Turks in Switzerland,Tusheti, Tutsi, Tuvaluans, Tuvans, Twa peoples, Txicao, Tzigane, U'wa,Ubykh, Udeghes, Udis, Ukrainian, Ukrainian American, UkrainianArgentine, Ukrainian Canadian, Ukrainian Russian, Ulchs, Ulster-Scots,Ulta, Umatilla, Umpqua, Upper Skagit, Urapmin, Ute, Uyghur, Uzbek,Valencian people, Vaturanga, Venda, Venetians, Veps, Vietnamese or Kinhor Jing or archaically Annamites, Vietnamese American, Vietnamese peoplein the United Kingdom, Vietnamese people in the Czech Republic,Vietnamese Norwegians, Vietnamese people in Bulgaria, Vietnamese peoplein Russia, Visayan, Vlachs, Volga Germans, Votes, Wa, Wabanaki,Waccamaw, Wailaki, Waitaha, Waiwai, Waki, Wakhs, Walla Walla, Walsers,Wampanoag, Wasco, Washoe, Wayana, Welayta people, Welsh, Welsh American,Welsh Australian, Welsh Canadian, Wends, White Mountain Apache, Wichita,Wintun, Wiyot, Wolof, Wu Chinese, Wyandot, Wyyanaha, Xakriabá, Xavante,Xerente, Xhosa, Xibe, Xikrin, Xin Uygurs, Xinh Mun, Xo Dang, Xtieng,Xucuru, Xueda, Yae, Yaghan, Yaghnabis, Yagua, Yakama or Yakimas,Yakughir, Yakuts, Yang, Yankton Sioux, Yanomami, Yao, Yavapai:Yavapai-Apache Nation, Yavapai-Prescott Indian Tribe, Yapese, Yaqui,Yawanawa, Yawalpiti, Yazgulamis, Yekuana, Yi, Yocha-Dehe, Yokut, Yoruba,Yörük, Yuchi, Yugur, Yukaghirs, Yuki, Yuma, Yumbri, Yupik, Yurok, Yupeople, Zaghawa, Zambo, Latino Zamboangueño, Zapotec, Zarma, Zeibeks,Zazas, Zhuang, Zou, Zulian, Zulu, or Zuni.

A sample grouping or characteristic can be gender. Gender can be male orfemale.

A sample grouping or characteristic can be socioeconomic status.Socioeconomic status can comprise, e.g., low, middle, or high.Socioeconomic status can be based on income, wealth, education, and/oroccupation.

A sample grouping or characteristic can be highest education level of asubject. Education level can be, e.g., kindergarten, primary (e.g.,elementary) school, middle school, secondary school (e.g., high school),college or university, junior college, graduate school, law school,medical school, or technical school.

A sample grouping or characteristic can be occupation-type. Anoccupation-type can be, e.g., healthcare, advertising, charity orvoluntary work, education, administration, engineering, environment,financial management or accounting, agriculture, legal, hospitality,human resources, insurance, law enforcement, business, aviation,fishing, tourism, media, mining, performing arts, publishing orjournalism, retailing, social care or guidance work, recreation,athletic, government, public service, science, or military, etc.

A sample grouping or characteristic can be annual income level. Annualincome level can be, e.g., about $0-$20,000; $20,000-$40,000;$40,000-$60,000; $60,000-$75,000; $75,000-$100,000; $100,000-$150,000;$150,000-$200,000; $200,000-$500,000; $500,000-$1,000,000;$1,000,000-$10,000,000; $10,000,000-$100,000,000; or more than$100,000,000. Annual income level can be about, more than about, or lessthan about $2500, $5000, $7500, $10,000, $12,500, $15,000, $17,500,$20,000, $22,500, $25,000, $27,500, $30,000, $35,000, $40,000, $50,000,$60,000, $70,000, $80,000, $90,000, $100,000, $125,000, $150,000,$200,000, or $250,000.

A sample grouping or characteristic can include a factor related todiet. Factors related to diet can include, e.g., daily caloric intake,types of food consumed (e.g., proteins, carbohydrates, fruits,vegetables, meats, dairy products, sweets, desserts, saturated fat,unsaturated fat, cholesterol, etc.), schedule of meal consumption, etc.

A sample grouping or characteristic can be geographic location of asubject. A geographic location can be a street address, a city block, aneighborhood in a town or city, a town or city, a metropolitan area, acounty, a state (e.g., any of the 50 states of the United States), acountry, a continent, or a hemisphere. A test subject and a normalindividual can live in the same geographic location.

A sample grouping or characteristic can be exposure to a disaster and/orenvironmental condition. A disaster or environmental condition can be,e.g., an earthquake, a hurricane, a blizzard, a flood, a tornado, atsunami, a fire, air pollution, water pollution, a terrorist attack, abioterrorist attack, radiation, nuclear attack, insect infestation, foodcontamination, asbestos, war, pandemic, lead poisoning, etc.

A sample from a test subject can be compared to a sample from one ormore normal subjects that share one or more sample characteristics withthe test subject.

EXAMPLES Example 1 Normal Cell Response to Erythropoietin (EPO) andGranulocyte Colony Stimulating Factor (G-CSF)

Normal cell signaling responses to EPO and G-CSF were characterizedthrough comparison to signaling responses observed in samples from asubclass of patients with myelodysplatic syndrome (MDS) referred toherein as “low risk” patients. Fifteen samples of healthy BMMCs (frompatients with no known diagnosis of disease) and 14 samples of BMMCsfrom patients who belonged to a subclass of patients withmyelodysplastic syndrome were used to characterize normal cell responsesto EPO and G-CSF. The 14 samples of low risk patients were obtained fromMD Anderson Cancer Center in Texas. The low risk patients were diagnosedas per standard of care at MD Anderson Cancer Center. The 15 samples ofhealthy BMMCs were obtained through Williamson Medical Center and from acommercial source (AllCells, Emeryville, Calif.). The samples obtainedthrough Williamson Medical Center were collected with informed consentfrom patients undergoing surgeries such as knee or hip replacements.

Each of the normal and the low risk samples were separated intoaliquots. The aliquots were treated with a 3 IU/ml concentration ofErythropoietin, a 50 ng/ml concentration of G-CSF and both a 3 IU/mlconcentration of Erythropoietin and a 50 ng/ml concentration of G-CSF.Activation levels of pStat1, pStat3 and pStat5 were measured using flowcytometry at 15 minutes after treatment with the modulators. In additionto the Stat proteins measured, several other elements were measured inorder to separate the cells into discrete populations according to celltype. These markers included CD45, CD34, CD71 and CD235ab. CD45 was usedto segregate lymphocytes, myeloid(p1) cells and nRBCs. The nRBCs werefurther segregated into 4 distinct cell populations based on expressionof CD71 and CD235ab: m1, m2, m3 and m4.

Distinct signaling responses were observed in the different cellpopulations; different activation levels of pStat1, pStat3 and pStat5were observed in EPO, G-CSF and EPO+G-CSF treated lymphocytes, nRBC1cells, myeloid(p1) cells and stem cells (data not shown). Although thiswas true in both the healthy and the low risk patients, the differentcell populations exhibited a much narrower range of induced activationlevels in normal samples than in the low risk samples. The differentcell populations also show a much narrower range of non-response to amodulator in normal cells. These observations accord with the commonunderstanding that diseased cells exhibit a wider range of differentsignaling phenotypes than normal cells. Additionally, celldifferentiation in disease may be inhibited or stunted, causing cells toexhibit characteristics such as signaling phenotypes that are differentfrom other cells of the same type.

Different activation levels of EPO, G-CSF and EPO+G-CSF-induced pStat1,pStat3 and pStat5 were observed in cell populations at various stages ofmaturation into red blood cells. The healthy samples exhibit much lessvariance in the activation levels of pStat1, pStat3 and pStat5 than thelow risk samples. Combining the modulators EPO and G-CSF does not alterthis observation; the combined response to the modulators still exhibitsless variance in the healthy cells. This result suggests that modulatorsmay be combined prior to modulation without distorting the activationstate data. These results demonstrate the utility of using the varianceof the observed activation levels as a metric for diagnoses and/orprognoses.

Example 2 Normal Cell Response to PMA and IFNa

Normal cell signaling responses to PMA and IFNa were characterized in aset of 12 normal samples. Twelve of the normal samples were obtainedfrom the National Institute of Health (NIH) and consisted ofcryopreserved leukapheresis peripheral blood mononuclear cell (PBMC)samples. The normal samples had been previously categorized as highpStat5 responders and low pStat5 responders by the NIH based onflow-cytometry based analysis of IFNa-induced pStat5 in isolated T cells(measured at 15 minutes after modulation). The set of samples comprised6 high responders and 6 low responders. The set of samples werehomogeneous by gender and were blind associated with race, age, genderand pStat5 response. Additionally, two normal samples comprisingcryopreserved PBMCs from healthy donors were processed at Nodality. Inaddition to the above described samples, a Jurkat cell line was used asa control.

Activation levels of different activatable elements were measured atdifferent time intervals after stimulation with PMA and IFNa. Inaddition to the activatable elements, several cell type markers wereused to segregate the single cell data for each sample into discretecell populations. Two different phosphorylation sites on pStat1(Y701 andS727) and pStat3 (Y705 and 5727) were measured. Unless otherwise noted,pStat1 and pStat3 activation discussed herein refers to pStat1(Y701) andpStat3 (Y705),

Cell surface markers and other markers such as Live/dead amine Aquastain were used to segregate the single cell data according to cellpopulations. First, live/dead amine Aqua stain was used to select forviable cells. CD14 was then used to segregate monocytes fromlymphocytes. SSC-A, CD20 and CD3 were used to segregate T cells, B Cellsand CD3-CD20-lymphocytes. CD4 was used to segregate T cells into CD4+and CD4− T cells. The percentage recovery from the samples, a metricthat compares the expected cell count to the actual cell count, wasdetermined. The percentage viability of the cells in the samples wasdetermined based on Aqua staining and the percentage of cells thatexpress cleaved PARP (a marker for apoptosis). The percentage of cellsthat exhibit higher than average auto-fluorescence was compared to thepercentage of cells that exhibit higher than average cleaved-PARPstaining.

The different cell populations demonstrated different responses tostimulation with PMA. pS6 and pERK response after stimulation with PMAin T cells, B cells and monocytes, respectively was observed.

Response to IFNa was also unique to the cell population being observed.The fold change in pStat1, pStat3 and pStat5 between IFNa stimulated andunstimulated cells over time after stimulation was determined (data notshown). The fold change of the activatable elements was measured at 1,15, 60, 120 and 240 minutes. In most of the cell populations andactivatable elements observed, the average fold change peaks at 15minutes post-stimulation. The fold change in pStat4, pStat6 and p-p38between IFNa stimulated and unstimulated cells from the normal sampleswas determined (data not shown). In most of the cell types observed, theaverage fold change peaks at 60 minutes. In this experiment, pStat4 isonly induced by IFNa in T cells (data not shown).

The IFNs-induced fold change in pStat1(S727) and pStat3(S727) inMonocytes, T cells and B cells from the normal samples was determined(data not shown). None of the different cell types demonstrated morethan a minimal activation of pStat1(S727) and/or pStat3(S727). TheIFNa-induced fold change in pStat1(S727) and pStat3(S727) in CD4+ andCD4− T cells was determined (data not shown). The magnitude of pStat5fold change was much larger in CD4+ T cells (average fold change 7.2)than in CD4− T Cells (average fold change 3.2).

The IFNa-induced fold change in pStat4, pStat6 and p-p38 in CD4+ andCD4− T cells from the normal samples was determined (data not shown).The magnitude of pStat4 fold change was much larger in CD4− T cells(average fold change 1.8) than in CD4+ T Cells (average fold change1.5).

The IFNa-induced activation of pStat1, pStat3, pStat5, pStat4, pStat6,p-p38, pStat3(S727) and pStat1(S727) in the Jurkat cells that were usedas a control was determined (data not shown). These cells demonstratedminimal IFNa-induced activation of pStat4, pStat6, p-p38, pStat3(S727)and pStat1(S727). IFNa-induced activation of pStat1, pStat3 and pStat5peaked at 15 minutes.

The IFNa-induced activation of pStat1, pStat3 and pStat5 in Jukat cells(control) and the T cells from the normal samples was determined (datanot shown). The magnitude of the pStat3 fold change in the Jurkat cells(average fold change=4.3) was much larger than in the T cells (averagefold change=3.2).

The relative frequencies of different cell sub-populations weredetermined (data not shown). IFNa-induced pStat1, pStat3, and pStat5 inmonocytes, T cells and B cells were compared (data not shown).IFNa-induced pStat1, pStat3, and pStat5 in samples from a Jurkat cellline was determined (data not shown). The different colored barsrepresent different plates of samples from which the activation levelsof IFNa-induced pStat1, pStat3, and pStat5 were measured. As shown inthe bar graphs, there was good agreement between the activation levelsin the two sets of control data.

The NIH Stat5 response classifications were determined (data not shown).These NIH response classifications were generated by stimulatingisolated T cells from the samples with IFNa and measuring pStat5response at 15 minutes. The agreement between the NIH responseclassifications and observed IFNa-induced pStat5 response was determined(data not shown). Of the 12 samples, the 3 samples with the highestIFNa-induced pStat5 response and the 3 samples with the weakestIFNa-induced pStat5 response corresponded with the NIH responseclassifications. However, the other samples did not agree. Thisdifference may be explained by the fact that the T cells were isolatedin the NIH experiment prior to characterizing pStat5 response, whereasin our analysis the T cells with modulated with pStat5 in aheterogeneous population of cells.

IFNa-induced pStat1, pStat3, and pStat5 in different cell populations asa function of the age of the person from whom the sample was derived wasdetermined (data not shown). IFNa-induced pStat1, pStat3, and pStat5 inMonocytes as a function of age was determined (data not shown).IFNa-induced pStat1, pStat3, and pStat5 in T cells as a function of agewas determined (data not shown). A strong T cell response wasconsistently observed in one of the samples (termed NIH10). IFNa-inducedpStat1, pStat3, and pStat5 in B cells as a function of age wasdetermined (data not shown). A strong B cell response was also observedin sample NIH10. These results illustrate the utility of sampling alarge range of normal patients to develop a model of normal activationlevels and using similarity rather than classification to characterizepatients. A classification model based on the samples would be skewed bythe high activation values observed in sample NIH10. However, asimilarity based model would account for the fact that NIH10 isdissimilar in activation level to the other normal samples.

Correlations between age and IFNa-induced pStat4 and pStat6 activationlevels were determined (data not shown). A positive correlation wasobserved between IFNa-induced pStat4 and age. A negative correlation wasobserved between IFNa-induced pStat6 and age. These results demonstratethat some induced activation levels for a test subject, e.g., anundiagnosed individual, can be normalized according to the age of theindividual prior to determining the similarity to normal samples.

IFNa-induced pStat1, pStat3 and pStat5 activation levels in monocytes, Bcells and T cells derived from normal samples from European Americans(ea) and African Americans (aa) were determined (data not shown). Nodifferences associated with race were observed.

The correlation between observed activation levels in the different cellpopulations in the normal samples were determined (data not shown). ThePearson correlation coefficient was calculated using difference metric(i.e., the difference between the Mean Fluorescence values in stimulatedand unstimulated samples) to represent the activation levels. Positivecorrelations greater than or equal to 0.5 and negative correlations lessthan or equal to −0.5 were determined. Generally, very high correlationwas observed between the pStat1, pStat3 and pStat5 in the B cells andthe T cells. The correlations between nodes in different cellpopulations were illustrated using a circular plot, where nodes with apositive correlation (>0.5) are connected by a red line and nodes with anegative correlation (<=−0.5) are connected by a green line.

The similarity in activation profiles between the normal samples weredetermined with heat maps (data not shown). The activation levels of thenodes in different cell populations were normalized by the maximum andminimum activation level (represented by the difference metric) for eachnode such that all nodes range from 0 to 1. Although little variance wasexhibited in the samples, this normalization method magnifies theexisting variance such that the samples may be analyzed to determinewhether there are distinct subgroups of normal samples. These resultssuggest that it may be helpful to build multiple models for normalsamples according to the different subgroups of response observed.

Example 3 Normal Cell Response to Varying Concentrations of GM-CSF,IL-27, IFNa and IL-6 in Whole Blood

Kinetic response to varying concentrations of modulators wasinvestigated in normal whole blood samples (i.e., samples from personswho have no diagnosis of disease). 11 normal samples were donated withinformed consent by Nodality Inc. employees and processed at NodalityInc. in South San Francisco, Calif. The samples were treated with 4different modulators (GM-CSF, IL-27, IFNa and IL-6) at 4 differentconcentrations of the modulator and activation levels of pStat1, pStat3and pStat5 were measured at different time points. Activation levelswere measured at 3, 5, 10, 15, 30 and 45 minutes using flowcytometry-based single cell network profiling. The concentrations of thestimulators are tabulated below in Table 2:

TABLE 2 Stimulator Concentrations low med hi GM-CSF 0.1 ng/ml 1 ng/ml 10ng/ml IL-27 1 ng/ml 10 ng/ml 100 ng/ml IFNa 1000 IU 4000 IU 100000 IUIL-6 1 ng/ml 10 ng/ml 100 ng/ml

The activation levels of pStat1, pStat3 and pStat5 were measured indiscrete cell populations as defined by cell surface receptorexpression. Gating was used to segregate the cells into discrete cellpopulations. In the gating analysis, SSC-A and FSC-A were first used tosegregate lymphocytes from non-lymphocytes. CD14 and CD4 were then usedto segregate the non-lymphocytes into populations of neutrophils andCD14+ cells (monocytes). CD3 and CD20 were then used to segregate thelymphocytes into populations of CD20+ (B Cells), CD3+ (T Cells) andCD20-CD3− cells. CD-4 was used to segregate the CD3+ T cells intopopulations of CD3+ CD4− and CD3+ CD4+ T cells.

The kinetic responses of different cell populations in the normalsamples were determined (data not shown). The activation levels observedin all of the donors over the time intervals at which they were measuredwere determined (data not shown). The activatable elements may havevarying responses based on the concentration of the modulator. Theactivation levels for the different samples showed little variationacross donors for the same concentration of IL-6. This suggests tightregulation of phosphorylation in normal cells.

The kinetic responses of different cell populations in the normalsamples were determined (data not shown). The line graphs contained inplot the activation levels observed in all of the donors over the timeintervals at which they were measured. The different concentrations ofIL-6 tabulated above are represented by different colored lines.Generally, the normal samples demonstrated similar activation profilesover time according to the concentration of sample given. Differentconcentrations of the modulator IL-6 yielded dramatically differentactivation profiles for some of the Stat phosphoproteins measured. Forexample, IL-6-induced pStat3 response varied at early time points (5-15minutes) for the different concentrations of IL-6 but became moreuniform at later time points. This uniformity of response supports theidea that normal cells exhibit a narrow range of activation. As thedifferent cell populations exhibited very different signaling profiles,these results also demonstrate the utility of segregating single-celldata into discrete cell populations prior to analysis.

Different cell populations demonstrated unique responses to modulation.The neutrophils exhibited very low IL-6 induced activation as comparedto the CD4+ T cells and monocytes. Between the CD4+ T cells andmonocytes, several differences in activation profiles were observed.Monocytes showed a peak activation of IL-6-induced pStat1 activity at adifferent time point than the CD4+ T cells. Although both the monocytesand the CD4+ T cells demonstrated a drop-off in pStat3 activity after 15minutes, the drop-off (post-peak or “resolution phase” activity) wasmuch more dramatic in the monocytes (data not shown). This observationconfirms the utility of using additional metrics which describe thedynamic response such as ‘slope’ and liner equations to representdynamic response to induced activation.

The different activation profiles for IFNa and IL-6-induced pStat1,pStat3 and pStat5 in T cells were compared (data not shown). IFNa canactivate all three Stats with activation profiles that are correlatedover time. This result implies that IFNa induced Stat profiles that arenot positively correlated may indicate dysregulation of Stat signalingor disease. In contrast, IL-6 induced Stat signaling did not showpositively correlated activation profiles over time.

Cell population dependent differences in IFNa induced and GM-CSF-inducedStat profiles were investigated (data not shown). IFNa-2b-inducedpStat1, pStat3 and pStat5 showed a range of activation profiles inmonocytes; there was little to no activation of IFNa-2b-induced pStat1and pStat5 in neutrophils (data not shown). The two cell populationsshowed more similar response to GM-CSF modulation. However, theactivation profiles indicate that neutrophils have prolonged activationphase of pStat5 responsive to G-CSF induction, whereas monocytesdemonstrate a resolution phase after 15 minutes.

GM-CSF, IFNa-2b, IL-6 and IL-27 induced pStat1, pStat3 and pStat5 inneutrophils, monocytes, CD4+ T cells, CD4− T cells, and Non B/T Celllymphocytes (NK) were investigated. These results demonstrate theutility of capturing different concentrations of different modulators atdifferent time points: many of cell populations that are uniquelyresponsive to different modulator and activation levels show littlevariance associated in some cell types/concentrations of modulators.Both of these properties allow for the characterization and modeling ofnormal cell activity. Unique response (including non-response) tomodulators based on cell type allows for the identification of aberrantdifferentiation and signaling dysregulation. Invariant responsesimilarly allows for the identification of outlier activation levelsthat may be associated with disease.

IL-6 induced activation of pStat4 in CD3+ CD4+ T cells was investigatedover time. Staining controls included bulk IFN-alpha dose response fromone donor. While different activation levels were associated with thedifferent concentrations of IL-5 at earlier time points, a convergenceof the activation levels at 15 minutes time was observed. Although thedifferent concentrations are still distinguishable at 15, 30 and 45minutes, the ranges observed with the different concentrationsdemonstrate far less variance. These data demonstrate activation rangesthat may serve as unique, low variance indicators of disease and/ordysregulation independent of the concentration of modulator used toinduce the activation levels.

Example 4 Functional Pathway Analysis of the Healthy Immune System

A greater understanding of the function of the human immune system atthe single cell level in healthy individuals can play a role indiscerning aberrant cellular behavior that can occur in settings such asautoimmunity, immunosenescence, and cancer. To achieve this goal, asystems-level approach capable of capturing responses of interdependentimmune cell types to external stimuli can be used. In this study, anextensive characterization of signaling responses in multiple immunecell subpopulations within PBMCs from a cohort of 60 healthy donors wasperformed using single cell network profiling (SCNP). SCNP can be amultiparametric flow-cytometry based approach that can enable thesimultaneous measurement of basal and evoked signaling in multiple cellsubsets within heterogeneous populations. In addition to establishingthe inter-individual degree of variation within immune signalingresponses, the possible association of any observed variation withdemographic variables including age and race was investigated. Usinghalf of the donors as a training set, multiple age- and race-associatedvariations in signaling responses in discrete cell subsets wereidentified, and several were subsequently confirmed in the remainingsamples (test set). Such associations can provide insight intoage-related immune alterations associated with high infection rates anddiminished protection following vaccination and into the basis forethnic differences in autoimmune disease incidence and treatmentresponse. SCNP allowed for the generation of a functional map of healthyimmune cell network responses that can provide clinically relevantinformation regarding both the mechanisms underlying immune pathologicalconditions and the selection and effect of therapeutics.

A systems-level approach can be used to provide a comprehensiveunderstanding of how the function of the human immune system arises fromthe interactions among numerous inter-connected components, pathways,and cell types. Reductionist approaches that analyze individualcomponents within the immune system have dominated in the past severaldecades primarily due to technological limitations. The recentdevelopment of high-throughput technologies is beginning to change thelandscape of immunological studies and researchers are ushering in thenew field of systems immunology (1). Here, a novel technology isdescribed that can have an enormous impact on this burgeoning fieldbecause it can allow for simultaneous functional measurements frommultiple cell subpopulations without the need for prior cell separation.This capability can enable a more integrated description of immunefunction than traditional studies which often focus on the behavior ofspecific cell types that have been physically isolated fromheterogeneous tissues such as peripheral blood, spleen, or lymph nodes.This technology was applied to the characterization of immune cellsignaling in healthy individuals to establish a reference functional mapin the context of an immune cell signaling network, which can be used toelucidate aberrant network-level behaviors underlying the pathogenesisof immune-based diseases.

SCNP can be a multiparametric flow-cytometry based analysis that cansimultaneously measure, at the single cell level, both extracellularsurface markers and changes in intracellular signaling proteins inresponse to extracellular modulators. Measuring changes in signalingproteins following the application of an external stimulus informs onthe functional capacity of the signaling network which cannot beassessed by the measurement of basal signaling alone (2). In addition,the simultaneous analysis of multiple pathways in multiple cell subsetscan provide insight into the connectivity of both cell signalingnetworks and immune cell subtypes (3). SCNP technology can be used toinvestigate signaling activity within the many interdependent cell typesthat make up the immune system because it can allow for the simultaneousinterrogation of modulated signaling network responses in multiple cellsubtypes within heterogeneous populations, such as PBMCs, without theadditional cellular manipulation that can be used for the isolation ofspecific cell types.

Summarized below are the results of an extensive characterization ofimmune cell signaling responses utilizing SCNP technology to quantifyphospho-protein levels (pStat1, pStat3, pStat5, pStat6, pAkt, pS6,pNFκB, and pErk) within pathways downstream of a broad panel ofimmunomodulators (including IFNα, IFNγ, IL2, IL4, IL6, IL10, IL27,α-IgD, LPS, R848, PMA, and CD40L) in seven distinct immune cellsubpopulations within PBMC samples from 60 healthy adults. Thissystems-level approach enabled the generation of a functional map ofimmune cell network responses in healthy individuals which serves as areference for understanding signaling variations that occur inpathological conditions such as autoimmunity and to inform clinicaldecision-making in vaccination and other immunotherapeutic settings. Inaddition, inter-subject variation in immune signaling responsesassociated with demographic characteristics of the healthy donors suchas age or race was identified.

Materials and Methods

PBMC Samples

Cryopreserved PBMC samples taken from 60 healthy donors within theDepartment of Transfusion Medicine, Clinical Center, National Institutesof Health with Institutional Review Board approval were used in thisstudy (Table 3). Blood donations from healthy donors, donated forresearch purposes with informed consent, were collected and processed asdescribed previously (4).

TABLE 3 Summary of donor numbers, age, race, and gender in the master,training, and test sample sets Master Training Test Number of 60 30 30Donors Mean Age 48.9 (19-73) yrs 47.9 (22-73) yrs 49.8 (Range) (19-73)yrs Gender 12 Female 5 Female 7 Female 48 Male 25 Male 23 Male Race 25African American 10 African American 15 African 34 European American 19European American American 1 Hispanic 1 Hispanic 15 European American 0Hispanic

SCNP Assay

Cryopreserved PBMC samples were thawed at 37° C. and resuspended in RPMI1% FBS before staining with amine aqua viability dye (Invitrogen,Carlsbad, Calif.). Cells were resuspended in RPMI 10% FBS, aliquoted to100,000 cells per well of 96-well plates, and rested for 2 h at 37° C.prior to 15 min 37° C. incubation with the following modulators: 1000IU/ml IFNα (PBL, Piscataway, N.J.); 250 ng/ml IFNγ, 50 ng/ml IL4, 50ng/ml IL10, α-IgD 5 μg/ml (BD, San Jose, Calif.); 50 ng/ml IL2, 50 ng/mlIL6, 50 ng/ml IL27, CD40L 0.5 μg/ml (R&D, Minneapolis, Minn.); R848 5μg/ml (Invivogen, San Diego, Calif.); LPS 1 μg/ml, PMA 40 nM (SigmaAldrich, St. Louis, Mo.). After exposure to modulators, cells were fixedwith paraformaldehyde and permeabilized with 100% ice-cold methanol aspreviously described (5). Methanol permeabilized cells were washed withFACS buffer (PBS, 0.5% BSA, 0.05% NaN₃), pelleted, and stained withfluorochrome-conjugated Abs. Abs used include α-CD3 (clone UCHT1), α-CD4(clone RPA-T4), α-CD45RA (clone HI100), α-CD20 (clone H1), α-pNFκB(clone K10-895.12.50), α-cPARP (clone F21-852), α-pStat1 (clone 4a),α-pStat3 (clone 4/p-Stat3), α-pStat5 (clone 47), α-pStat6 (clone18/p-Stat6), α-pErk (clone 20A) [BD, San Jose Calif.]; α-pAtk (cloneD9E), α-pS6 (clone 2F9) [CST, Danvers, Mass.]; and α-CD14 (clone RMO52)[Beckman Coulter, Brea, Calif.].

Flow Cytometry Data Acquisition and Analysis

Flow cytometry data was acquired using FACS DIVA software (BD, San Jose,Calif.) on two LSRII Flow Cytometers (BD, San Jose, Calif.). All flowcytometry data were analyzed with WinList (Verity House Software,Topsham, Me.). For all analyses, dead cells and debris were excluded byforward scatter (FSC), side scatter (SSC), and amine aqua viability dye.PBMC subpopulations were delineated according to an immunophenotypicgating scheme (not shown).

SCNP Terminology and Metrics

The term “signaling node” can refer to a specific protein readout in thepresence or absence of a specific modulator. For example, a response toIFNα stimulation can be measured using pStat1 as a readout. Thissignaling node can be designated “IFNα→pStat1”. Each signaling node canbe measured in each cell subpopulation. The cell subpopulation can benoted following the node, e.g., “IFNα→Stat1 B cells”. Two differentmetrics are utilized in this study to measure the levels ofintracellular signaling proteins in either the unmodulated state or inresponse to modulation. The “Basal” metric is used to measure basallevels of signaling in the resting, unmodulated state. The “Fold” metricis applied to measure the level of a signaling molecule after modulationcompared to its level in the basal state. The Equivalent Number ofReference Fluorophores (ERFs), fluorescence measurements calibrated byrainbow calibration particles on each 96-well plate, serve as a basisfor all metric calculations (6, 7).

The “Basal” and “Fold” metrics were calculated as follows:

log₂[ERF(Unmodulated)/ERF(Autofluorescence)]  Basal:

(log₂[ERF(Modulated)/ERF(Unmodulated)]+Ph−1)/Ph  Fold:

Where Ph is the percentage of healthy [cleaved PARP (poly ADP-ribosepolymerase) negative] cells

Statistical Analysis

The high dimensionality of the SCNP data for individual nodes (i.e.,combination of cell populations, modulators, and protein readouts)greatly increases the probability of finding chance associations in thedata (i.e., false discovery). To address this issue, a multi-stepanalysis strategy designed to reduce the chance of false discoveries, byaccounting for multiple testing and therefore reducing the chance of aType 1 Error (incorrectly rejecting the null hypothesis) was followed.First, the data was split into training (30 samples) and test sets (30samples) stratified randomly on race and age (Table 3). Multivariatelinear regression was then used to find associations between individualimmune signaling nodes and age and/or race in the training set.Associations with immune signaling were found by controlling for age andrace. The exact form of the linear model used to test for significantassociations between age, race and node signaling in the training dataset was:

SignalingNode|Population=α₁+Age*β₁+Race*β₂

Where Race was coded as (1=African American, 0=European American).Linear models were built for each signaling node in each of thefollowing cell subpopulations: monocytes, B cells, naïve helper T cells,naïve cytotoxic T cells, memory helper T cells, and memory cytotoxic Tcells. In the training data set, signaling nodes were considered to havea significant association with age for models in which β₁ has asignificant p-value (<0.05) and a significant association for race formodels in which β₂ has a significant p-value (<0.05). Discovering groupsof signaling nodes rather than individual nodes can guard againstfinding chance associations. To create groupings of nodes, a principalcomponent analysis (PCA, (8)) was performed both on the set of immunesignaling nodes found to be significantly associated with age and alsowith the set of immune signaling nodes found to be significantlyassociated with race from the linear models in the training data. ThePCA analysis accounted for correlation among signaling nodes, which cancarry redundant information, by creating linear combinations ofsignaling nodes associated with age and/or race. In addition, to confirmthe age and race associations in the test set a Gatekeeper strategy wasused to control the Type 1 Error rate (9). In this strategy, eachhypothesis to be validated in the test set can be pre-specified andsequentially ordered and subsequently tested in that order. A hypothesiscan be considered validated if it is significant in the test set and allother hypotheses tested prior to it are significant. For this study,models using the first principal component from the age PCA and thefirst principal component from the race PCA were tested in the test set.The principal component models for age and race which were locked (i.e.,the model coefficients and PCA loadings matrices were locked) in thetraining set before being tested on the test set (in order) were of theform:

Race=α₁+NodePC ₁*β₁+Age*β₂

NodePC ₁=α₁+Age*β₁+Race*β₂

Only the first principal components were tested since both firstprincipal components for both the age and race PCA both accounted forapproximately 50% of the variance in training data. Only after theconfirmation of the principal components in the test set were thecontributions of the individual signaling nodes to the principalcomponents for age and race associations examined, to understand thebiology associated with age and/or race.

Correlations Between Signaling Nodes

R software (version 2.12.1) was used to compute Pearson correlationcoefficients between all pairs of signaling nodes within and betweeneach of the seven distinct cell subpopulations. Heatmaps were generatedin Excel 2007 (Microsoft, Redmond, Wash.).

Results

Cell-type-specific Patterns of Immune Signaling Responses in PBMCs fromHealthy Donors

Thirty eight signaling nodes, or specific protein readouts in thepresence or absence of a specific modulator (Table 4), were measured in12 cell populations defined by their surface phenotypes including 7distinct immune cell subpopulations (monocytes, B cells,CD3-CD20-lymphocytes (NK cell-enriched subpopulation), naïve helper Tcells, memory helper T cells, naïve cytotoxic T cells, and memorycytotoxic T cells, (data not shown)) within unsorted PBMC samples from60 healthy donors (Table 3) using two different metrics [Basal and Fold(Materials and Methods)].

TABLE 4 Thirty-eight signaling nodes measured in the study. Allsignaling nodes were measured in each immune cell subpopulation.Signaling Node 1 IFNα → pStat1 2 IFNα → pStat3 3 IFNα → pStat5 4 IFNα →pStat6 5 IFNγ → pStat1 6 IFNγ → pStat3 7 IFNγ → pStat5 8 IFNγ → pStat6 9IL2 → pStat5 10 IL2 → pStat6 11 IL4 → pStat5 12 IL4 → pStat6 13 IL6 →pStat1 14 IL6 → pStat3 15 IL10 → pStat1 16 IL10 → pStat3 17 IL27 →pStat1 18 IL27 → pStat3 19 IL27 → pStat5 20 IL27 → pStat6 21 α-IgD/LPS →pS6 22 α-IgD/LPS → pAkt 23 R848 → pErk 24 R848 → pNFκB 25 CD40L → pErk26 CD40L → pNFκB 27 PMA → pS6 28 PMA → pErk 29 Unmodulated → pStat1 30Unmodulated → pStat3 31 Unmodulated → pStat5 32 Unmodulated → pStat6 33Unmodulated → pS6 34 Unmodulated → pAkt 35 Unmodulated → pErk 36Unmodulated → pNFκB 37 Unmodulated (DMSO) → pS6 38 Unmodulated (DMSO) →pErk

When gating on the viable cells (defined by scatter properties and amineaqua as described in Materials and Methods) only 15 of the 28 modulatedsignaling nodes showed a signaling response above the threshold level ofFold >0.25 representing an approximately 1.2 fold change in modulatedlevels relative to basal (see Materials and Methods), and a level ofsignaling that is very reproducible (data not shown). In contrast, whengating separately in the same samples on the 7 distinct immune cellsubpopulations, 23 of these nodes showed induced signaling in at leastone of the 7 subpopulations (data not shown), exemplifying the utilityof SCNP in the identification of heterogeneous functionality in complextissues and rare cell populations.

Other examples support this conclusion (data not shown). The TLR ligandR848 (Resiquimod) can be an immunomodulator that can portray cell-typespecificity, and consistent with this induced pErk and pNfκB only in Bcells and monocytes, immune cell subpopulations known to express thereceptors (TLR7/8) for this ligand. In contrast to R848, IFNα can be aglobally active immunomodulator due to the ubiquitous expression of theIFNα receptor on immune cells. As expected, at least one pStat proteinwas activated in response to IFNα in all of the immune cellsubpopulations (data not shown) and this global responsiveness wasreflected in the data from the Viable Cell population. Due to thegenerally reduced signaling responses from the more heterogeneousparental populations, in the sections below, data is reported primarilyfor the 7 distinct immune cell subpopulations.

Since the SCNP assay allows for an actual quantification of signalingresponses, by measuring the degree of pathway activity for each node ineach cell subpopulation, differential levels of activation in thedifferent immune cell subtypes was observed. For example, as expected,modulation of PBMCs with IFNγ produced the highest level of pStat1 inmonocytes, lower levels in B cells, and a much weaker pStat1 response inT cells (with differential levels of activation among the latter, i.e.,naïve T cell subsets showing a higher level of response than theirmemory counterparts (data not shown). In contrast to IFNγ treatment, IL2modulation of PBMCs led to pStat5 activation primarily inCD3-CD20-lymphocytes and T cells, again with differential activationlevels seen among the T cell subsets and no effects on monocytes and Bcells (data not shown).

Variation in Immune Signaling Responses in PBMCs from HealthyIndividuals

For each of the 38 signaling nodes tested in the assay (listed in Table4), the range of signaling responses in each immune cell subset acrossthe 60 samples was quantified (data not shown). A comparison of the dataobtained from the analysis of the training set and the test set revealedthat, as expected, the distributions in the training and test set didnot differ significantly for a majority of the signaling responses(p>0.05 for 98.9% of the 38 signaling nodes measured within each of the7 distinct cell subsets). Although there was a narrow range of responsesfor the majority of the signaling nodes measured within the 7 distinctcell subsets, considerable inter-donor variation was observed for asubset of the modulated nodes (data not shown).

Immune Cell Signaling Network Map in PBMCs from Healthy Individuals

A functional map of the healthy immune cell signaling network wasgenerated by calculating the Pearson correlation coefficients betweenpairs of nodes within and between each of the 7 distinct immune cellsubpopulations. Overall, visualization of the healthy immune cellsignaling network map revealed a high frequency of positively correlatedsignaling responses (data not shown). Cytokine-induced signalingresponses within each subpopulation were highly positively correlated,with a notable exception occurring for the naïve cytotoxic T cell subsetfor which IL10 and IL2 signaling responses were uncorrelated or weaklyinversely correlated with responses to other cytokines (data not shown).Positive correlations among cytokine signaling responses were alsopresent across different cell subpopulations with the strongestinter-subpopulation correlations generally occurring between pairs ofnodes within the different T cell subsets. Intra-subpopulationcorrelations among cytokine-induced signaling responses and amongPMA-induced signaling responses were weakest within the B cell subset,although strong positive correlations were present for signalingresponses downstream of CD40L and between responses downstream of IgDcrosslinking in this subpopulation.

Age and/or Race as Variables Associated with Immune Signaling Responses

Both age and race are known to be relevant to clinical outcomes inimmune based disorders (10-12). Demographic heterogeneity of the 60donor cohort (Table 3) allowed us to assess the association betweenimmune signaling responses and age and/or race. Given the largedimensionality of the SCNP data for individual nodes (i.e., combinationof cell populations, modulators, and protein readouts) the possibilityof chance association (i.e., false discovery) is high. To address thisissue, we followed a multi-step analysis strategy. First, the data wassplit into training (30 samples) and test sets (30 samples) randomlystratified on race and age. Multivariate linear regression was then usedto find associations between individual immune signaling nodes and ageand/or race in the training set. Because discovering groups of signalingnodes can guard against chance associations, a principal componentanalysis (PCA) was performed both on the set of immune signaling nodesassociated with age and the set of signaling nodes associated with race.The PCA analysis accounted for the previously observed correlation amongsignaling nodes by combining the correlated signaling nodes associatedwith age or race in the training set. For confirmation of associationsin the test set, a Gatekeeper strategy was used. The first principalcomponent for both the age and race PCAs in the training set were lockedand applied to the test set in a pre-specified order and significancelevel (p<0.05). Only after the confirmation of the principal componentsin the test set were the contributions of the individual signaling nodesto the principal components for age and race associations examined, tounderstand the biology associated with age and/or race.

The PCA for age-associated immune signaling was performed on 19signaling responses found to be associated with age, controlled forrace, in the training set (p<0.05, Table 5).

TABLE 5 Summary of age-associated signaling nodes identified in thetraining set. All age-associated responses identified in the trainingset are shown, and nodes which were confirmed in the test set arehighlighted in gray. A negative slope indicates a negative correlationwith age. Training Test Age Age p- Age Age p- Node|Population R² slopeValue R² slope Value IFNα → pStat1|Naïve cytotoxic T cells 0.434 −0.0140.000 0.129 −0.012 0.069 IFNα → pStat3|Naïve cytotoxic T cells 0.249−0.006 0.013 0.043 −0.003 0.399 IFNα → pStat5|Naïve cytotoxic T cells0.325 −0.013 0.002 0.206 −0.016 0.017 IFNα → pStat6|Memory helper Tcells 0.644 −0.002 0.031 0.003 0.000 0.875 IFNγ → pStat1|Naive cytotoxicT cells 0.422 −0.007 0.000 0.131 −0.005 0.074 IL10 → pStat3|Naivecytotoxic T cells 0.201 0.010 0.022 0.059 0.005 0.368 IL2 → pStat5|Naivehelper T cells 0.539 0.027 0.000 0.201 0.023 0.022 IL2 → pStat6|Naivehelper T cells 0.291 −0.007 0.011 0.122 0.004 0.176 IL27 → pStat1|Naivecytotoxic T cells 0.310 −0.026 0.010 0.076 −0.017 0.168 IL27 →pStat5|Naive cytotoxic T cells 0.234 −0.010 0.011 0.222 −0.009 0.016IL27 → pStat6|Naive cytotoxic T cells 0.278 −0.003 0.049 0.009 −0.0010.678 IL4 → pStat6|Naive cytotoxic T cells 0.187 −0.012 0.026 0.234−0.013 0.020 IL6 → pStat1|Naive cytotoxic T cells 0.342 −0.009 0.0020.129 −0.008 0.074 IL6 → pStat3|Naive cytotoxic T cells 0.340 −0.0160.003 0.082 −0.014 0.148 PMA → pErk|B cells 0.201 0.009 0.040 0.005−0.001 0.816 PMA → pErk|Naïve helper T cells 0.331 0.012 0.026 0.005−0.001 0.816 Unmodulated → pS6|Memory cytotoxic T cells 0.199 −0.0020.020 0.028 −0.001 0.519 Unmodulated (DMSO) → pS6|Memory cytotoxic Tcells 0.167 −0.002 0.036 0.064 −0.001 0.208 Unmodulated → pStat1|Memorycytotoxic T cells 0.201 0.002 0.038 0.114 0.001 0.245

The first principal component for age accounted for 45% of the variance.Examination of the 19 individual signaling nodes revealed that one ofthese responses (PMA→pErk|B cells) was within the B cell subpopulation,while all of the remaining responses were within T cell subsets with thehighest number occurring within the naïve cytotoxic T cell subset. Only3 unmodulated nodes (Unmodulated→pS6|Memory cytotoxic T cells,Unmodulated (DMSO)→pS6|Memory cytotoxic T cells, andUnmodulated→pStat1|Memory cytotoxic T cells, Table 5) were found to beassociated with age in the training set.

The PCA for race-associated immune signaling included 18 signalingresponses found to be associated with race, controlled for age, in thetraining set (p<0.05, Table 6).

TABLE 6 Summary of race-associated signaling nodes identified in thetraining set. All of the race-associated responses identified in thetraining set are shown, and nodes which were confirmed in the test setare highlighted in gray. A positive slope indicates nodes that were moreresponsive in AAs than in EAs. Training Test Race Race p- Race Race p-Node|Population R² slope Value R² slope Value IFNα → pStat3|Memorycytotoxic T cells 0.224 0.140 0.016 0.133 −0.054 0.314 IFNα →pStat3|Memory helper T cells 0.198 0.110 0.030 0.111 −0.018 0.751 IFNα →pStat5|Monocytes 0.343 0.100 0.025 0.015 −0.038 0.534 IFNα →pStat5|Naïve helper T cells 0.293 0.170 0.047 0.182 −0.117 0.280 IFNγ →pStat1|Memory helper T cells 0.234 0.060 0.048 0.032 −0.015 0.699α-IgD + LPS → pAkt|B cells 0.386 −0.390 0.001 0.265 −0.347 0.006 α-IgD +LPS → pS6|B cells 0.277 −0.680 0.008 0.228 −0.617 0.018 IL10 →pStat1|Memory helper T cells 0.187 0.050 0.024 0.097 −0.038 0.126 IL10 →pStat3|Memory cytotoxic T cells 0.244 0.280 0.018 0.034 −0.084 0.439IL10 → pStat3|Memory helper T cells 0.174 0.200 0.047 0.003 −0.026 0.816IL27 → pStat1|Memory cytotoxic T cells 0.288 0.350 0.008 0.028 −0.0040.975 IL27 → pStat3|Memory cytotoxic T cells 0.357 0.240 0.003 0.008−0.026 0.671 IL6 → pStat1|Memory cytotoxic T cells 0.335 0.080 0.0020.044 −0.030 0.278 IL6 → pStat3|Memory cytotoxic T cells 0.297 0.2900.006 0.031 −0.051 0.406 IL6 → pStat3|Memory helper T cells 0.182 0.2800.031 0.014 −0.057 0.717 R848 → pNFκB|B cells 0.279 −0.090 0.021 0.040−0.027 0.579 R848 → pNFκB|Memory helper T cells 0.258 0.030 0.016 0.1210.008 0.603 Unmodulated → pStat5|Memory cytotoxic T cells 0.568 0.0390.043 0.017 −0.002 0.881

The first principal component for race accounted for 54% of thevariance. The 18 race-associated signaling responses consisted of aslightly more diverse set of cell subpopulations than the age-associatedresponses and included responses to several cytokines, the TLR ligandR848, and IgD crosslinking. Only one unmodulated node(Unmodulated→Stat5|Memory cytotoxic T cells) was associated with race inthe training set.

The first principal component for age (locked from the training set) wassignificant in the test set (p<0.05), confirming that age can explainsome of the observed inter-donor variation in immune signalingresponses. After confirmation, this first principal component wasdissected by inspecting the loadings matrix and whether or not the nodewas significant in both the test and training set, to further examinethe underlying biology. Four individual signaling responses(IFNα→pStat5|Naïve cytotoxic T cells, IL27→pStat5|Naive cytotoxic Tcells, IL4→pStat6|Naive cytotoxic T cells, IL2→pStat5|Naive helper Tcells, Table 5) were found to have high loadings and were significantlyassociated with signaling in the test set as well. Of note, none of theunmodulated nodes with age-associations in the training set wereindividually significant in the test set. Exemplifying the SCNP assayadvantage of subpopulation analysis, we confirmed that the IL4→pStat6signaling node demonstrated a statistically significant decrease withage specifically within naïve cytotoxic T cells (data not shown; Table5). A trend of decreasing signaling response with age was seen one levelup the population hierarchy in the overall cytotoxic T cells, but thisassociation was dampened by the memory cytotoxic T cells whoseIL4→pStat6 signaling response showed no association with age and thusdid not reach statistical significance in the overall cytotoxic T cellsubset (data not shown). All 3 signaling nodes within the naïvecytotoxic T cell compartment (IFNα→pStat5, IL27→pStat5, and IL4→pStat6)were positively correlated with each other and all showed decreasedresponsiveness with age (Table 5, data not shown), while IL2→pStat5activation within naive helper T cells increased with age and wasuncorrelated with the three naïve cytotoxic T cell signaling nodes(Table 5, data not shown).

The race model, based on the first principal component for race (lockedfrom the training set), was also significant in the test set (p<0.05),confirming that race is associated with differences in immune signalingresponses (data not shown). After confirmation, this first principalcomponent was also dissected to further examine the underlying biology.Two individual race-associated responses had high loadings and weresignificant in both the test and training sets. Both of these werewithin the B cell population (α-IgD/LPS→pAkt and α-IgD/LPS→pS6 nodes,data not shown, Table 6) and both showed greater levels ofresponsiveness in the European American (EA) donors than in the AfricanAmerican (AA) donors (data not shown), and they were highly correlated(r=0.81).

Defining the range of immune signaling activity in multiple immune cellsubsets and establishing an overall map of the immune cell signalingnetwork in healthy individuals can be used as a first step in providinga baseline for the characterization of aberrant signaling responses andchanges in the immune signaling network architecture that occur indiseases such as cancer and autoimmune disorders. Because the immunesystem consists of multiple interdependent cell types whose behavior ismediated by complex intra- and inter-cellular regulatory networks, acomprehensive description of healthy immune function can use asystems-level approach capable of integrating information from multiplecell types, signaling pathways, and networks. In this Example, SCNP wasused to perform a broad functional characterization of the healthyimmune cell signaling network. As expected, many of the immunomodulatorsincluded in this study evoked cell-type specific responses (data notshown), highlighting the complexity of the regulation of biologicalfunction during immune responses. For a subset of the modulators andspecific cell types investigated in this study, differential receptorexpression and/or differential activation patterns have been previouslyreported. In instances where such data is available, the cell-typespecific signaling responses described here are generally consistentwith those reports (13-15).

To gain insight into the connectivity of the immune cell signalingnetwork, node-to-node correlations within and between each of thedistinct immune cell subpopulations were mapped. A high-level analysisof this map revealed an abundance of positively correlated nodes, with ahigher frequency of positive correlations for node-to-node pairs withinthe same immune cell subset than for pairs of nodes spanning differentcell types (data not shown). Very few nodes were inversely correlatedwith the most notable exceptions occurring for IL10- and IL2-inducedresponses which showed weak inverse correlations with othercytokine-induced signaling responses specifically within the naïvecytotoxic T cell subset. This map can be compared with those generatedusing samples from patients with immune-based disorders to identifychanges in the network architecture that occur under pathologicalconditions, and can be applied to the analysis of samples obtainedlongitudinally from treated patients to monitor individual responses totherapeutics.

Aging is often accompanied by a deterioration of the immune system,resulting in a higher susceptibility to infections and lower efficacy ofvaccination in the elderly population (16-18). Given the multitude ofage-associated alterations in the function of the immune system, withsome of the most profound occurring in T cells subsets (18, 19), it washypothesized that age may have an impact on the cell signaling responsesmeasured in this study.

The results shown here demonstrate that some of the variation in healthyimmune signaling responses can in fact be attributed to donordemographic characteristics such as age or race. Specifically, theanalysis provided herein of the impact of age on immune signalingresponses has revealed 4 individual signaling nodes with significantassociations with age. Strikingly, all 4 of the individualage-associated immune signaling responses identified here were withinnaïve T cells, a cell type which has been previously reported to undergoage-related functional changes such as reduced proliferation andcytokine production (18).

The majority (3 of 4) of the individual age-associated signaling nodesconfirmed in the PCA analysis and with statistical significance in bothtraining and test sets occurred within the naïve cytotoxic T cellsubset, while only 1 of the 4 resided in the naive helper T cell subset.One of the most dramatic age-related changes in the cytotoxic T cellsubset is a decrease in the frequency of naïve cytotoxic T cells withage (19, 20), and this was also observed in the samples analyzed in thisstudy (data not shown). Additionally, we have observed an age-relateddecline in JAK-STAT signaling activity in the naïve cytotoxic T cellsubset in response to multiple cytokines including IFNα, IL4, and IL27(Table 5). Signaling elicited by these cytokines plays a role incytotoxic T cell survival, proliferation and differentiation (21-24).Thus, the observed age-related decrease in responsiveness to thesecytokines may underly some of the functional changes within thecytotoxic T cell compartment. For example, loss of the costimulatoryreceptor CD28 occurs frequently with increasing age (19) and theresultant CD28-cytotoxic T cells show reduced proliferation, resistanceto apoptosis, and higher expression of effector proteins. In addition, ahigh frequency of CD28-cytotoxic T cells has been shown to correlatewith decreased responses to vaccination (25).

The single naïve helper T cell age-associated signaling node was anincreased IL2-induced activation of Stat5 (Table 5). This signalingpathway is required for T cell proliferation and activation (26, 27),and both the production of IL2 and the proliferation of naïve helper Tcells have been shown to decrease with age (28). The data reported heresuggest that the use of IL2 can be an effective strategy for rescuingnaïve helper T cell proliferation in the elderly.

Overall, the results reported here provide evidence of age-associatedalterations in T cell cytokine signaling responses, with the moststriking differences occurring specifically within the naïve cytotoxic Tcell subset. While age-associated differences in T cell signalingthrough the TCR have been widely reported (29), relatively few studieshave documented age-related differences in human T cell cytokinesignaling (30). Further, much of the work that has been conducted toexamine associations between T cell cytokine signaling responses and agehas been performed using isolated T cells with techniques such asWestern blot analysis that allow for only population-level measurementsof pathway activation. Analyses performed at the level of total T cellsmay fail to capture age-associated alterations specific to a given Tcell subset.

The age-associated naïve T cell cytokine signaling responses identifiedhere can play a role in age-related increase in susceptibility toinfection, decline in vaccine responsiveness, and the prevalence ofcertain autoimmune diseases.

Differences in signaling between AAs and EAs, the two major ethnicgroups with sufficient representation in this study cohort forstatistical analysis, were examined. Because ethnic-related differenceshave been reported in the prevalence of autoimmune diseases such assystemic lupus erythematosus (31) and multiple sclerosis (32) and inresponse rates to immunotherapies such as IFNα (10), Benlysta/belimumab(11), and stem cell transplantation (12), it was hypothesized that someof the variation in immune signaling responses may be attributable toracial differences among the study donors. Our assessment ofrace-associated signaling responses revealed that BCR- (α-IgD) inducedPI3K pathway activity was significantly higher in EAs than in AAs. WhileBCR crosslinking can lead to the activation of multiple signalingpathways, BCR-mediated activation of the PI3K pathway has been shown toprovide signaling that plays a role in B cell survival (33). Thus, thedifferences in PI3K pathway activity observed here can result in racialdifferences in B cell fate in response to BCR stimulation.

Controlling for ethnicity is emerging as a key component in assuring theaccuracy of clinical diagnostics (34) and in selecting treatments (11).For example, AAs and EAs infected with hepatitis C virus have been shownto differ in their response rates to IFNα-based therapy (35) and thishas been shown to correlate with in vitro IFNα response profiles (36).

This work demonstrated the utility of the SCNP technology in providing asystems-level description of immune signaling responses withininterdependent immune cell subpopulations. Applying this approach to thecharacterization of immune cell signaling in a cohort of healthy donorsallowed for the quantification of the range of signaling across donorsand revealed tight ranges for the immune signaling responses measuredsuggesting that the activation of these signaling nodes can be highlyregulated in healthy individuals. Although inter-subject differences inimmune signaling responses were generally quite low, within the subsetof nodes that displayed the most substantial inter-donor variation someof the variation in immune signaling pathway activation could beattributed to differences in demographic factors such as age or race.Overall, the healthy immune cell signaling network map generated hereprovides a reference for comparison with network maps generated underdisease-associated conditions, using samples from patients at baselineor over the course of therapeutic intervention to identify immunenetwork restructuring that is thought to occur under therapeuticpressure and to guide therapeutic selection.

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Example 5

Overview:

Given the biologic and clinical heterogeneity inherent to AML, an unmetmedical need exists for tools to guide the choice of drugs most relevantto the underlying biology of the individual AML. Single Cell NetworkProfiling (SCNP) can be used as a tool to inform biology-based clinicaldecision making including therapy selection and disease monitoring.Previous studies have provided preliminary proof-of-concept on theutility of SCNP to dissect the pathophysiologic heterogeneity ofhematologic tumors and assess their differential response to singleagent and combination therapies. This study characterizes the signaltransduction networks implicated in the growth and survival of AML cellsand how those are affected by in vitro exposure to various FDA-approvedand investigational therapeutic agents. Compounds were selected based ontheir ability to disrupt key mechanisms of AML tumor cell growth andsurvival.

Design:

This study used peripheral blood or bone marrow samples (n=9), which hadbeen previously ficoll separated and cryopreserved. Patientcharacteristics are shown Table 7. One cryovial per patient was used.Samples were thawed and centrifuged over ficoll to remove dead cells anddebris.

TABLE 7 Patient Characterisics Reference Disease Sample TimepointReceipt date Age Usage 1910-006 AML Pre-induction Dec. 2, 2010 36 1 vial(10 million cells) 1910-008 AML Post-induction Dec. 2, 2010 47 1 vial(10 million cells) Resistant 1910-011 AML Post-induction Feb. 18, 201152 1 vial (10 million cells) Resistant 1910-013 AML Relapse On TherapyJan. 15, 2011 60 1 vial (10 million cells) 1910-015 AML Pre-inductionJan. 19, 2011 83 1 vial (10 million cells) 1910-016 AML Post-inductionFeb. 23, 2011 37 1 vial (10 million cells) Resistant 1910-017 AMLPre-induction Feb. 9, 2011 71 1 vial (10 million cells) 1910-018 AMLRelapse Off Therapy Feb. 10, 2011 66 1 vial (10 million cells) 1910-019AML Pre-Induction Apr. 13, 2011 24 1 vial (10 million cells)

Samples were split to perform the following:

Arm #1 assessed basal and modulated signaling in the JAK/STAT,PI3K/mTor, and MEK/ERK pathways in the presence and absence of specifickinase inhibitors. Kinase inhibitors were added 1 hr before the additionof the signaling stimulus. Signaling was induced by individual additionof stem cell factor, Flt3 ligand, G-CSF, IL-3, or thrombopoietin (TPO)for a short period of time (5-15 min). Cells were then fixed,permeabilized, and stained with a cocktail of cell surface andphospho-specific antibodies to measure signaling in multiple cell types.Signaling data is calculated in each cell type using a fold-changemetric comparing each condition to its basal state: example:(stimulated^(+/− inhibitor))/(unstimulated). Also, cells with anapoptotic phenotype were excluded from the signaling analysis by gating.

Arm #2 assessed the cytotoxic and cytostatic impact of various drugs assingle agents and in combinations (including the specific kinaseinhibitors tested in arm #1). Here the cells from each donor werecultured in the presence of TPO, IL-3, SCF, and FLT3L for 2 days todrive proliferation. After 2 days the cells were then distributed intowells containing various drugs, wherein the cells were cultured for 48hours. The cultures were fixed, permeabilized, and stained with acocktail of antibodies to measure complete cell death, apoptosis, S/G2phase, M-Phase, and DNA damage. These readouts were also obtained fromsamples cultured separately with individual growth factors (no drugs)for 4 days.

A schematic of the experiment is shown in FIG. 3.

Examples of reports for a subject (#1910-017) are shown in FIGS. 8, 9,and 10. In FIG. 8A, a cell lineage diagram is depicted. Percentages ofcell types are show for subject #1910-017 (circle on graph, e.g., seeFIG. 8B) and for healthy or normal cells (bar on graph). The reportdepicts fold activation of activatable elements relative to a basalstate in radar plot form to allow comparison of the subject sample withfold activation ranges for normal samples (see e.g., FIG. 8B). Foldactivation is indicated for samples that were or were not contacted witha kinase inhibitor. FIGS. 8B, 8C, 8D, and 8E show information fordifferent cell types

Another form of a report is depicted in FIG. 9. FIG. 9A indicatespercentages of cells in a ring diagram. The outer circle corresponds tocells in the #1910-017 AML sample of PBMCs pre-induction. The innercircle corresponds to percentages of cells in healthy bone marrow. Thepercentages do not add up to 100%, as some types cells are not included.Fold change from basal state of cell signaling is indicated as a heatmap.

For CD34+ cells, patient #1910-017 has high basal p-AKT level that isattenuated by PI3K/mTor inhibitor, but not FLT3 inhibitor. This suggeststhat the high basal level is not a function of high FLT3 activity. Thereis also a high p-STAT5 basal level. There is no FLT3L or G-CSFresponses, which are observed in healthy CD34+ cells. The CD34-CD117+cell population has a similar signaling phenotype as the CD34+ cells.The CD34-CD117-cells respond strongly to TPO, but not to G-CSF. Thelymphocytes have no signaling. High basal level of p-STAT5 signaling isinhibited by CP-690550.

The report indicates drug responses. The response to AC220 is not knowndue to no FLT3L induced signaling in #1910-017. With respect toGDC-0941, there is partial inhibition of SCF-pAKT and pS6. With respectto AZD-6244, there is complete inhibition of SCF-pERK, partialinhibition of pS6, and no inhibition of pAKT. With respect to BEZ235,there is complete inhibition of SCF induced pAKT, and partial inhibitionof pS6. With respect to CP-690550, there is complete inhibition of IL-3signaling, and partial inhibition of TPO signaling.

FIG. 9D shows growth factor dependent effects on cell growth andsurvival. Survival and cell growth appear independent of growth factorstimulation.

FIGS. 9D and 9E show drug induced apoptosis and cytostasis. In general,this patient's myeloid cells resisted apoptosis for most drugs,including AraC. However, inhibition of cell cycle (M-phase) was observedfor many drugs. Proteosome inhibition (bortezomib) induced considerablelevels of cell death and cytostasis. HSP90 inhibitor also inducedapoptosis.

FIG. 10 shows another example of a report for a subject (#1910-017).FIG. 10 illustrates information on percentage of cell types (based onsurface phenotype) in a sample from the subject and percentages of celltypes in normal or healthy cells (see e.g., FIG. 10G). FIG. 10 containsbiological information on the cell types (see e.g., FIG. 10B).Information on signaling phenotypes are illustrated as radar plots (seee.g., FIGS. 10C, 10D, 10E, and 10F). The report in FIG. 10 also containsinformation on cell growth and cell survival and cytostasis after drugexposure.

Example 6 Healthy Bone Marrow FLT3 Pathway Signaling

Healthy bone marrow myeoblasts (BMMb) display similar FLT3L inducedsignaling while AML samples display a range of responses. These dataallow for comparison of leukemic to healthy responses.

FLT3 ligand induced signaling of p-S6, p-Erk, p-Akt, and p-Stat5 at 5,10, and 15 min time points in healthy bone marrow myeloblats (BMMb), andleukemic blasts from AML donors with or without FLT3-ITD (internaltandem duplication) mutation are shown in FIG. 4. FLT3-ITD AML with highmutational load responses are more homogenous than FLT3-WT AML (FIG. 4).

A PCA (principal component analysis) of healthy BMMB, FLT3-TD, andFLT3-WT samples illustrate homogeneity of BMMB and FLT3-ITD mutatedsamples and heterogeneity of FLT3-WT samples. Distinct signalingpatterns were seen among groups.

FLT3 WT donors are more heterogeneous than FLT3 ITD donors and showdistinct patterns. Some signal like Healthy BMMb; some signal likeFLT3-ITD AML; some signal like neither group. Donors with low mutationalload stand out from FLT3-ITD group. Comparison of AML to Healthy BMMbidentifies AML donors that behave similar to or distinct from HealthyBMMb. (see FIG. 5)

Example 7 Impact of Time from Blood Draw to Peripheral Blood MononuclearCell (PBMC) Processing and Cryopreservation on Functional PathwayActivity as Measured by Single Cell Network Profiling (SCNP) Assays

Cryopreserved peripheral blood mononuclear cells (PBMCs) can beroutinely used in biomarker development studies. Multiple pre-analyticparameters related to blood draw, processing, and cryopreservation canimpact the quality of PBMC samples used in functional assays. Singlecell network profiling (SCNP) can be a multi-parametric flow cytometrybased approach that can measure intracellular signaling activity inresponse to extracellular modulators. Preservation of cell viability andfunctionality plays a role in the performance of the SCNP assay. Inother immunological assays, such as the ELISpot assay, the length oftime from blood draw to PBMC cryopreservation can affect assayperformance. In this study, the effect of time from sample collection tocryopreservation on functional pathway activation was assessed bycomparing SCNP assay readouts in paired PBMC samples processed within 8or 32 hrs from blood draw.

Forty mLs of peripheral blood was obtained for 20 donors (10 male/10female, 60-83 yrs) at the Stanford Blood Center. Half of the samplevolume from each donor was processed within 8 hrs of blood draw [Day 1(D1)], and the remainder left at 25° C. overnight [Day 2 (D2)]. For D2samples, PBMC isolation and cryopreservation were initiated 24 hrs fromthe processing start time of the corresponding D1 sample. For the SCNPassay, samples were thawed, modulated for 15 mins with 12immunomodulatory stimuli (interferons, interleukins, TLR ligands, etc.),fixed, and permeabilized. Permeabilized cells were stained withfluorochrome-conjugated antibodies recognizing extracellular surfacemarkers or intracellular signaling molecules (pStat1, pStat3, pStat5,pS6, pNFκB, pAkt, and pErk). Thirty eight signaling nodes (readouts ofmodulated signaling) were measured in 7 distinct immune cell subsets(monocytes, B cells, NK cells, naive/memory helper T cells, andnaive/memory cytotoxic T cells).

Analysis of paired PBMC samples revealed that D1 and D2 samples had nosignificant difference in the percentage of healthy cells (measured bythe percentage of cleaved PARP″ cells) and no difference insubpopulation frequencies (as a percentage of parent populations) forthe majority of the 7 subsets examined. A numerically small butstatistically significant decrease in the percentage of healthy cells inD2 compared to D1 samples was observed for B cells, NK cells, and naivehelper T cells (mean difference 5.6%, 5.8%, and 3.2% respectively,p<0.05) while the monocyte subset was the only one to show a significantdecrease (9.4%, p<0.05) in frequency (as a percentage of parent) on D2.Similar intracellular signaling pathway modulation responses wereobserved for D1 and D2 samples (FIG. 6A), although the majority of nodesdisplayed lower modulated responses in D2 samples (10.0% mean decreasebetween D1 and D2). A good correlation (Spearman r>0.5) between D1 andD2 was observed for the majority (63%) of responsive signaling nodes.Within each dataset, inter-node correlation coefficients were calculatedto generate immune signaling network maps. Comparing these maps showedgood agreement between the correlations measured within each dataset[mean difference of −0.01 between inter-node correlations across days(D1 mean correlation 0.21, D2 mean correlation 0.20)] demonstratingbiological consistency between the 2 datasets in the structure of theimmune signaling network. Further, 13 age-associated differences(p<0.05) in immune signaling responses were identified in the D1 datasetand the majority of these remained significant in the D2 dataset(p<0.05). For example, several cytokine signaling responses within naïvecytotoxic T cells had a significant decrease with age in both datasets(FIG. 6B).

These results demonstrate that blood samples processed the day followingblood draw provide meaningful information on functional pathwayactivation using the SCNP assay and support the identification ofstatistically significant associations with clinical variables such asage. In a clinical setting, overnight shipping of patient samples to thelab performing the test may be required.

Example 8 Stimulus-Specific and Cell-Subset-Specific Inter-DonorVariation in Immunological Signaling Responses in Healthy Individuals

Single cell network profiling (SCNP) can be a multi-parameter flowcytometry based approach that can allow for the simultaneousinterrogation of intracellular signaling pathways in multiple cellsubpopulations within heterogeneous tissues such as peripheral blood orbone marrow. The SCNP approach is well-suited for characterizing themultitude of interconnected signaling pathways and immune cellsubpopulations that interact to regulate the function of the immunesystem. Recently, SCNP was applied to generate a functional map of the“normal” human immune cell signaling network by profiling immunesignaling pathways downstream of a broad panel of immunomodulators inmultiple immune cell subsets within peripheral blood mononuclear cells(PBMCs) from a large cohort of healthy donors. In this study, anin-depth analysis of the inter-donor variation in normal immunesignaling responses was performed. This analysis demonstrated that thedegree of inter-donor variation in immune signaling responses does notvary directly with the magnitude of the response. Instead, cellsubpopulation-specificity and stimulus-specificity in the degree ofinter-donor response heterogeneity was observed. Further, an analysis ofvariation in signaling activity at the single cell level revealed thatinter-donor variation in immune signaling responses may arise primarilydue to donor-to-donor differences in the proportion of responding cellsor, alternatively, due to inter-donor differences in the intensity ofthe response from relatively homogeneously responding subpopulations.The characterization of normal inter-donor variation in immune signalingpathway activation presented here provides a basis for identifyingimmune signaling abnormalities in immune-mediated diseases.

Introduction

The human immune system is composed of a complex network of cell typesand signaling pathways that, in healthy individuals, can interact toprovide immunity against pathogens and tumor-associated antigens whilesimultaneously preventing detrimental immune responses to self-antigen.Deregulation of immune cell signaling network responses can result inaberrant immune function leading to increased susceptibility to diseasessuch as autoimmunity, chronic infections, and cancer. Because immuneresponses can be governed by a network of distinct cell types,systems-level analyses that measure the activity of intracellularsignaling networks within multiple immune cell types can provide moreclinically relevant insight into the basis of immune-mediated disordersand the effects of therapeutic intervention on the function of theoverall immune system than traditional immunological studies which focuson the behavior of a specific immune cell subset following isolationfrom complex tissues such as peripheral blood, lymph nodes, or thespleen.

Single cell network profiling (SCNP) is a flow-cytometry based approachthat is well-suited for investigating how the immune system responds andreacts to external stimuli at a network-level, because the SCNP approachcan allow for the simultaneous interrogation of modulated signalingactivity across multiple signaling pathways in multiple interdependentimmune cell subpopulations. The SCNP technology has been appliedextensively to disease characterization and patient stratification inhematological malignancies such as acute myeloid leukemia (AML) andchronic lymphocytic leukemia (CLL) (1-3).

More recently, SCNP technology was applied to generate a functional mapof “normal” human immune signaling responses to provide a reference foridentifying signaling abnormalities in pathological conditions such asautoimmunity. To generate the “normal” immune signaling network map,SCNP was used to profile signaling pathways downstream of a broad panelof immunomodulators (including interferons, interleukins, IgDcrosslinking, TLR ligands, and CD40L) in seven distinct, non-sortedimmune cell subpopulations within peripheral blood mononuclear cells(PBMCs) from a large cohort of healthy individuals (see Example 4).While the majority of the immune signaling nodes measured in the“normal” immune signaling network mapping displayed a relatively narrowrange of responses across the cohort of healthy donors, a subset of theimmune signaling responses displayed considerable inter-donor variation.

A greater understanding of the degree of donor-to donor variation inimmune signaling responses across healthy donors can be used todetermine which immune signaling responses in cells from diseased donorscan be classified as abnormal. Establishing inter-donor variation inimmune signaling responses from healthy individuals that can beattributed to differences in demographic factors such as age, race, orgender can provide insight into the basis for disparities in theprevalence of immune-mediated disease among different donor subgroups.Notably, some of the inter-donor variation in immune signaling responsessurveyed in the “normal” immune signaling mapping can be attributed todifferences in demographic factors such as age and race.

Here, an in-depth analysis of the degree of inter-donor variation inimmune signaling network responses was performed to assess patterns inthe distribution of signaling nodes which displayed high heterogeneityacross the healthy donor cohort. This analysis revealed that the degreeof inter-donor variation did not vary directly with the magnitude of theresponse. In addition, high inter-donor variation was not restricted toa specific cell type or modulator. Instead, the level of inter-donorheterogeneity in the activation of a given signaling molecule wasdependent both on the stimuli used to modulate the signaling protein andon the immune cell subpopulation in which the signaling molecule wasactivated. Further, this study demonstrated that inter-donorheterogeneity in modulated signaling activity from a given signalingcomponent within a specific cellular subpopulation can be driven by auniform subpopulation response of differing intensities across donors,or alternatively, can arise due to differences in the frequency ofresponsive cells (subpopulation heterogeneity) across the donors. Thesefindings have implications for the characterization of immune signalingabnormalities in pathological conditions such as autoimmunity andcancer.

Results

Global Analysis of Inter-Donor Variation

Intracellular signaling activity across multiple immune cellsubpopulations was analyzed using single cell network profiling (SCNP)as described in Example 4. The phosphorylation status of 8 signalingproteins (Stat1, Stat3, Stat5, Stat6, Akt, S6, Erk, and NFκB) wasmeasured in response to 12 stimuli (IFNα, IFNγ, IL2, IL4, IL6, IL10,IL27, α-IgD, LPS, R848, PMA, and CD40L) in seven distinct(non-overlapping) immune cell subpopulations (monocytes, B cells,CD3-CD20-lymphocytes (natural killer cell-enriched subpopulation), naïvehelper T cells, memory helper T cells, naïve cytotoxic T cells, andmemory cytotoxic T cells) within unsorted PBMC samples from 60 healthyindividuals. The Fold metric (Materials and Methods) was utilized tomeasure the levels of intracellular signaling proteins in response tomodulation, and the interquartile range (IQR) for the Fold was used toquantify the degree of inter-donor variation for each signaling node(readout of modulated signaling, see Materials and Methods) in eachimmune cell subpopulation.

A global analysis of the inter-donor variation in immune signalingresponses was performed by determining which signaling responsesdisplayed relatively high inter-donor variation using the average IQR(0.03) as a threshold. Notably, all of the signaling responses thatdisplayed modulated activity above a threshold of Fold >0.25(representing an approximately 1.2 fold change in modulated levelsrelative to basal levels, see Materials and Methods). Thus, perturbingthe immune signaling network allows for the detection of donor-to-donorheterogeneity that is more substantial than the inter-donorheterogeneity that is observed from the unperturbed network.

Although high inter-donor hetereogeneity was confined to signalingresponses that showed a response above the 0.25 Fold threshold value,the degree of inter-donor variation did not vary directly with themagnitude of the response. Thus, it was sought to determine if highinter-donor variation was restricted to specific immune cellsubpopulations and/or to responses to specific immunomodulators. Foreach of the cell subpopulations, the percentage of responsive signalingnodes that showed high inter-donor variation was calculated. Thisanalysis revealed that, of the signaling nodes that modulated, a greaterpercentage of these signaling responses showed high inter-donorheterogeneity in the T cell subpopulations and CD3-CD20-lymphocytes thanin the monocytes and B cells. Next, to assess which modulators producedresponses with high inter-donor variation, the percentage of responsivesignaling nodes that that showed high inter-donor variation wasdetermined for each stimulus. For a few of the modulators, such asBCR/LPS, PMA, and IL2, all or most of the responses displayed highinter-donor variation. However, for the majority of the modulators, thedegree of inter-donor variation in the responses differed amongst thedifferent cell subsets and amongst the different phospho-proteinreadouts. For example, modulation with IFNγ resulted in pStat1 responseswith high inter-donor variation in monocytes and B cells, but not in thenaïve T cell subsets, and IFNγ-induced pStat3 and pStat5 showed lowinter-donor variation in monocytes unlike the IFNγ-induced p-Stat1responses in this subpopulation.

Stimulus-Specific Inter-Donor Variation in Immune Signaling

As discussed previously, the IQR did not vary directly with Fold acrossthe full panel of signaling nodes measured in all of the immunesubpopulations. Thus, it was next investigated whether there was adirect relationship between Fold and the IQR for responses by a specificphospho-protein readout within a given immune subpopulation acrossmultiple stimuli. Inter-donor variation in pStat1 signaling did not varydirectly with the magnitude of the pStat1 response, but insteaddisplayed stimulus-specificity. To assess the validity of thisobservation, the values of the Fold and the degree of inter-donorvariation for half of the donors randomly assigned to a training setwere compared with the values for the second half of the donors assignedto a test set (Materials and Methods). The values were remarkablyconsistent across both donor sets confirming the observation ofstimulus-specificity in inter-donor hetereogeneity (data not shown).

Cell Subset-Specific Inter-Donor Variation in Immune Signaling

Next, the relationship between the degree of inter-donor heterogeneityand the magnitude of the response for a specific signaling node acrossmultiple cell subpopulations was analyzed. There is not a directrelationship between the degree of inter-donor variation and themagnitude of the pStat5 response (data not shown). In both training andtest data sets, the IQR for naïve helper T cells is extremely highdespite a relatively moderate Fold for this cell subset (data notshown). In addition, CD3-CD20-lymphocytes, memory cytotoxic T cells,naïve cytotoxic T cells, and memory helper T cells display similardegrees of inter-donor variations despite differences in the intensityof the pStat5 response in each of these subsets. Thus, the inter-donorvariation in IL2-induced pStat5 displayed cell-type specificity and didnot vary directly with the magnitude of the pStat5 response in each celltype.

Single Cell Analysis Reveals Cell Subpopulation Heterogeneity

Use of flow cytometry can allow for the quantification of immunesignaling responses in each of the individual cells in a givenpopulation or subpopulation. Notably, the IL2-induced pStat5 responsesshowed strong bimodality, where a portion of the cells in eachsubpopulation show elevated pStat5 levels following IL2 treatment whilea subset of the cells overlap with the basal pStat5 distribution (datanot shown). Interestingly, the frequency of IL2 responsive cells in eachof the T cell subpopulations varied from donor to donor. Further, theinter-donor variation in IL2-induced pStat5 Fold values (data not shown)are driven primarily by differences in the proportion of cells thatrespond to IL2 rather than the intensity of the response in theresponsive subset (data not shown). In contrast to the bimodal pStat5responses observed following IL2 stimulation, the T cell subpopulationsdisplayed unimodal pStat5 levels following stimulation with IFNα. Forthe IFNα→pStat5 signaling node, the inter-donor differences weredetermined primarily by the intensity of the pStat5 responses overrelatively homogenous subpopulations. Thus, the results shown heredemonstrate that inter-donor variation in immune signaling responses canarise due to inter-donor differences in the degree of subpopulationheterogeneity or due to inter-donor differences in the responsemagnitudes from homogeneously responding subpopulations.

Discussion

Immune responses can be regulated by a complex network of diverse celltypes and interconnected signaling pathways. Deregulation of the immunesystem can lead to dampened immune responses to pathogens and tumorcells (immunodeficiency), excessive immune responses to innocuousforeign antigens (hypersensitivity), or to inappropriate responses toself-antigens (autoimmunity). A greater understanding of the alterationsin the immune cell signaling network that underlie immune-mediateddiseases can lead to improved methods for diagnosing and treating suchdiseases. However, determining which immune signaling responses fromdiseased patients can be classified as abnormal can involvecomprehensive knowledge of the immune cell signaling network in thebaseline, or disease-free, state. Recently, single cell networkprofiling (SCNP) was applied to generate a functional map of the normalimmune cell signaling network by measuring immune signaling responses toa broad panel of immunomodulators in multiple immune cell subpopulationswithin PBMCs from a large number of healthy individuals (See Example 4).This “normal” characterization can provide a basis for comparison withdiseased specimens to identify, within the immune cell signalingnetwork, which responses differ significantly from the baseline stateand which responses are within the normal range of variation.

In this study, the distribution of the degree of inter-donor variationwas analyzed in immune signaling responses across the normal immune cellsignaling network. The results of this analysis have revealed thatimmune signaling responses with relatively high inter-donor variation,as quantified by the interquartile range (IQR), are not confined tospecific immune cell subsets or to intracellular signaling responses tospecific immunomodulators. The immune signaling responses that displayedhigh inter-donor variation were, however, restricted to the subset ofimmune signaling responses that showed activation above a relatively lowthreshold. These results highlight the role of applying a perturbationto probe the functional capacity of the immune system and to revealdonor-to-donor differences in the behavior of the immune cell signalingnetwork.

Although high inter-donor variation was restricted to immune signalingresponses that showed some degree of modulated activity, there was not adirect linear relationship between the magnitude of the response and thedegree of inter-donor variation for the full panel of immune signalingresponses (data not shown). In addition, when the analysis of therelationship between response magnitude and inter-donor variation in theresponse was restricted to a specific signaling node within a specificimmune cell subpopulation, the degree of inter-donor variation, again,did not vary directly with the magnitude of the response. Instead, thedegree of inter-donor heterogeneity displayed stimulus-specificity (datanot shown). Likewise, narrowing the analysis of the relationship betweenresponse magnitude and inter-donor variation in the response to theactivation of a specific intracellular protein by a specific modulatorrevealed cell subpopulation-specificity in the degree of inter-donorvariation and, again, a poor correlation between the level ofinter-donor variation and the response magnitude (data not shown). Thus,these results demonstrate that the degree of normal human variation inimmune signaling is not generalizable for a given protein readout,immunomodulator, or cell subpopulation. These findings have implicationsfor the identification of immune signaling responses that may haveutility as clinical biomarkers for diagnosis, prognosis, and treatmentselection in immune-mediated pathologies.

Because the SCNP workflow involves measuring signaling activity usingflow cytometry, this approach allowed for an investigation of thevariation in signaling activity among individual cells withinphenotypically defined immune cell subpopulations. An analysis ofsignaling activity at the single-cell level revealed bimodality inIL2-induced Stat5 phosphorylation, even with relatively well-defined Tcell subpopulations (data not shown). This subpopulation heterogeneityin IL2 responsiveness may be driven by variation in the expression ofthe IL2 receptor. Recent work has shown that expression levels of theIL2R subunits (IL2Rα, IL2Rβ, and IL2Rγ) can vary substantially in clonalT cell populations (4). Thus, considerable variation may be expected fornon-clonal T cell subpopulations.

Notably, the frequency of IL2 responsive cells within each subpopulationvaried widely from donor to donor with relatively small donor-to-donordifferences in the pStat5 intensities for the responsive cells (data notshown). Thus, the high inter-donor variation in the IL2-induced pStat5Fold values can be due to differences in the frequency of responsivecells. Assessing the inter-donor and intra-subpopulation variations inIL2-induced Stat5 phosphorylation in immune subpopulations withinpatient samples can have clinical relevance given the use of IL2 as animmunotherapy for the treatment of metastatic melanoma and renal cellcarcinoma. Because high dose IL2 therapy can be associated with severetoxicity and only a subset of patients respond to treatment with IL2(5), the identification of biomarkers for predicting response to IL2immunotherapy can have high clinical utility.

Subpopulation heterogeneity in signaling activity was also observedfollowing treatment with α-IgD in the B cell subpopulation (data notshown). For this modulator, the presence of responsive andnon-responsive B cells was expected due to the lack of IgD expression ona portion of B cells (i.e. immature B cells and class-switched B cells).In contrast to IL2 and α-IgD responses, the majority of the signalingnodes that were profiled in this study displayed relatively homogeneous(unimodal) responses within the seven distinct immune cellsubpopulations (data not shown). The observed homogeneity in signalingfor many of the immune signaling nodes surveyed here may be reflectiveof relatively homogeneous expression of the corresponding receptorsacross each of the seven immune cell subpopulations.

In summary, the degree of normal inter-donor variation in theresponsiveness of a given phospho-protein readout can be highly specificto both the immunomodulator used to generate the response and the cellsubpopulation in which the response is measured. Quantifying the normalvariation in immune signaling responses within the immune cell signalingnetwork can play a role in establishing normal baseline ranges againstwhich diseased specimens can be compared and thus provides a foundationfor the discovery of biomarkers that can aid in the diagnosis, treatmentselection, and clinical monitoring of diseases such as cancer andautoimmunity.

Materials and Methods

PBMC Samples

Sixty cryopreserved peripheral blood mononuclear cell (PBMC) samplestaken from healthy donors within the Department of Transfusion Medicine,Clinical Center, National Institutes of Health were used in this study.Blood donations from healthy donors were collected and processed asdescribed previously (6).

SCNP Assay and Flow Cytometry

The SCNP assay and flow cytometry data acquisition and analysis wereperformed as previously described (see Example 4). Briefly,cryopreserved PBMC samples were thawed at 37° C. and re-suspended inRPMI 1% FBS before staining with amine aqua viability dye (Invitrogen,Carlsbad, Calif.). Cells were re-suspended in RPMI 1% FBS, aliquoted to100,000 cells per condition, and rested for 2 hours at 37° C. prior toincubation with modulators at 37° C. for 15 minutes. After exposure tomodulators, cells were fixed with paraformaldehyde and permeabilizedwith 100% ice-cold methanol. Methanol permeabilized cells were washedwith FACS buffer (PBS, 0.5% BSA, 0.05% NaN₃), pelleted, and stained withantibody cocktails containing fluorochrome-conjugated antibodies againstphenotypic markers for cell population gating and up antibodies againstintracellular signaling molecules. Flow cytometry data was acquiredusing FACS DNA software (BD Biosciences, San Jose, Calif.) on two LSRIIFlow Cytometers (BD Biosciences, San Jose, Calif.). Flow cytometry datawas analyzed with WinList (Verity House Software, Topsham, Me.). For allanalyses, dead cells and debris were excluded by forward scatter (FSC),side scatter (SSC), and amine aqua viability dye. PBMC subpopulationswere delineated according to an immunophenotypic gating scheme.

SCNP Terminology and Metrics

The term “signaling node” can refer to a specific protein readout in thepresence or absence of a specific modulator. For example, the responseto IFNα stimulation can be measured using pStat1 as a readout. Thissignaling node can be designated “IFNα→pStat1”. Each signaling node canbe measured in each cell subpopulation. The cell subpopulation can benoted following the node, e.g., “IFNα→pStat|B cells”. The “Fold” metriccan be applied to measure the level of a signaling molecule aftermodulation compared to its level in the basal state. The EquivalentNumber of Reference Fluorophores (ERFs), fluorescence measurementscalibrated by rainbow calibration particles, serve as a basis for allmetric calculations (7-9).

The “Fold” metric can be calculated as follows:

(log₂[ERF(Modulated)/ERF(Unmodulated)]+Ph−1)/Ph  Fold:

Where Ph is the percentage of healthy (cleaved PARP negative) cells

Training and Test Set Subdivision

The data set for the 60 donors was split into both training and testsets. Thirty donors each were randomly assigned to the test and trainingset. Manual inspection of the data sets ensured that they wererelatively balanced according to age and race.

REFERENCES

-   1. Kornblau S M et al. (2010) Dynamic single-cell network profiles    in acute myelogenous leukemia are associated with patient response    to standard induction therapy. Clin. Cancer Res 16:3721-3733.-   2. Rosen D B et al. (2010) Functional characterization of FLT3    receptor signaling deregulation in acute myeloid leukemia by single    cell network profiling (SCNP). PLoS ONE 5:e13543.-   3. Rosen D B et al. (2010) Distinct patterns of DNA damage response    and apoptosis correlate with Jak/Stat and PI3kinase response    profiles in human acute myelogenous leukemia. PLoS ONE 5:e12405.-   4. Feinerman O et al. (2010) Single-cell quantification of IL-2    response by effector and regulatory T cells reveals critical    plasticity in immune response. Mol. Syst. Biol 6:437.-   5. Antony G K, Dudek A Z (2010) Interleukin 2 in cancer therapy.    Curr. Med. Chem 17:3297-3302.-   6. Pos Z et al. (2010) Genomic scale analysis of racial impact on    response to IFN-alpha. Proc. Natl. Acad. Sci. U.S.A. 107:803-808.-   7. Purvis N, Stelzer G (1998) Multi-platform, multi-site    instrumentation and reagent standardization. Cytometry 33:156-165.-   8. Shults K E et al. (2006) A standardized ZAP-70 assay—lessons    learned in the trenches. Cytometry B Clin Cytom 70:276-283.-   9. Wang L, Gaigalas A K, Yan M (2011) Quantitative fluorescence    measurements with multicolor flow cytometry. Methods Mol. Biol    699:53-65.

Example 9 Single Cell Network Profiling (SCNP) Reveals Race-AssociatedDifferences in B Cell Receptor Signaling Pathway Activation

Race-related differences have been documented in the incidence ofautoimmune diseases such as systemic lupus erythematosus and multiplesclerosis, in the clinical response to immunotherapies [such as IFNα (inHCV infections) and belimumab (in systemic lupus erythematosus)] and tohematopoietic stem cell transplantation. However, the basis for suchrace-associated differences remains poorly understood. Single CellNetwork Profiling (SCNP) can be a multiparametric flow cytometry basedapproach that can simultaneously measure intracellular signalingactivity in multiple cell subpopulations. Previously, SCNP analysis ofperipheral blood mononuclear cells (PBMCs) from 60 healthy donorsidentified a race-associated difference in αIgD induced levels of p-S6and p-Akt in B cells. The present study extended this analysis to abroader range of signaling pathway components downstream of the B cellreceptor (BCR) in European Americans and African Americans using asubset of donors from the previously analyzed cohort of 60 healthydonors.

Thirty five BCR signaling nodes (a node is defined as a paired modulatorand intracellular readout) were measured by SCNP in PBMCs from 10healthy donors [5 African Americans (36-51 yrs), 5 European Americans(36-56 yrs), all males]. Cryopreserved PBMCs were thawed, modulated at37° C. in 96-well plates, fixed and permeabilized. Permeabilized cellswere stained with fluorochrome-conjugated antibodies that recognizeextracellular surface markers and intracellular signaling molecules. Thelevels of seven phospho-proteins [p-Lck (Y505), p-Syk (Y352), p-Akt(S473), p-S6 (S235/S236), p-p38 (T180/Y182), p-Erk (T202/Y204), andp-NFκB (S529)] were measured in CD20+ B cells at 0, 5, 15, 30, and 60minutes post αIgD exposure. CD20 and IgD surface markers were used todetermine the frequency of IgD+ B cells.

Analysis of BCR signaling activity in European American and AfricanAmerican PBMC samples revealed that, compared to the European Americandonors, B cells from African Americans had lower αIgD inducedphosphorylation of multiple BCR pathway components, including themembrane proximal proteins Syk and Lck as well as proteins in the PI3Kpathway (S6 and Akt), the MAPK pathways (Erk and p38), and the NFκBpathway (NFκB) (see example for αIgD induced p-S6 levels in FIG. 7A).Overall, 4 (p-Syk, p-S6, p-Akt, and p-Erk) of the 7 BCR pathwaycomponents tested (averaged over all timepoints for each donor) showedstatistically significant differences in αIgD induced activation levelsbetween racial groups (p=0.016, Wilcoxon test). Analysis of thefrequency of IgD+ B cells showed that PBMCs from African Americans had alower frequency of IgD+ B cells than PBMCs from European Americans[(p=0.016, Wilcoxon test), FIG. 7B, and that the frequency of IgD+ Bcells had a strong positive correlation with BCR pathway activation(i.e. Pearson correlation coefficient r>0.6 for most BCR signalingnodes). While race-associated differences in the frequency of IgD+ Bcells were detected, the levels of IgD expression (as measured by themedian fluorescence intensity) in the IgD+ B cell subpopulation did notdiffer between the races (p=0.286). Thus, the race-related difference inBCR pathway activation is attributable, at least in part, to arace-associated difference in IgD+ B cell frequencies.

In conclusion, SCNP analysis allowed for the identification ofstatistically significant race-associated differences in BCR pathwayactivation within PBMC samples from healthy donors.

Example 10 Normal Non-Diseased Responses to Genotoxic Stress UsingHealthy PBMC

FIG. 11 shows normal PMBC DNA damage kinetics to double strand breaksinduced by etoposide, Ara-C/Daunorubicin, and Mylotarg. FIG. 12 showsnormal PBMC Myeloid DNA Damage Kinetics to Double Strand Breaks inducedby Etoposide, Ara-C/Daunorubicin, or Mylotarg. FIG. 13 shows normal PBMCLymph and Myeloid response to Ara-C/Daunorubicin: (kinetics and effectof Daunorubicin dose) measuring DNA Damage Response and Daunorubicinfluorescence.

FIG. 14 shows that AML samples display a range of DDR responses comparedto Normal Healthy Non-Diseased CD34+ Myeloblasts. AML DNA DamageResponse (DDR) to double strand break inducing agents Ara-C/Daunorubicinor Etoposide at 6 h: AML display a range of DDR Responses; some higherthan normal myeloblasts; many lower than normal myeloblasts. There isevidence of defective DDR/drug metabolism in individual patients. Forexample, normal Myeloblasts CD34+ display a larger induction of DDR thannormal mature Myeloid cells (CD34−, D11b+). Also, CD34+ AML blasts tendto have higher DDR responses yet still display a wide range of p-Chk2induction.

Etoposide has faster kinetics than Ara-C/Daunorubicin, Mylotarg. Thepeak read was around 2 hours. pATM peaks at 2 h, then diminishessignificantly. pChk2 peaks at 1 h but remains detectable after 2 h. P53and pH2AX stay at similar levels across kinetic timecourse.

After 4 h for Ara-C/Daunorubicin: a) all readouts increase with time; b)Daunorubicin Dose makes a large difference. Mylotarg (gemtuzumabozogamicin, G0) has faster kinetics in Myeloid vs Lymphoid cells. G0 isan immunotoxin that targets CD33+ Myeloid cells. (See e.g., U.S. PatentPublication No. 20100099109). Induction of pATM, pChk2, is seen inMyeloid cells by 2 h. Some downregulation of pATM is seen at 6 and 8 h.Induction of pH2AX and p53 increase with time, and larger effects areseen after 4 h.

In summary, multiple components of DNA Damage Repair machinery can bequantified across time in normal healthy cell populations.

Example 11 Single Cell Network Profiling (SCNP) Reveals Age- andDisease-Based Heterogeneity in Healthy Individuals and in Patients withLow Risk (LR) Myelodysplastic Syndrome (MDS)

Background:

Normal hematopoiesis changes with age through unknown mechanisms. Lowrisk myelodysplasia is characterized by cytopenias arising throughinefficient hematopoiesis. It was hypothesized that both of thesedifferences might result from changes in responsiveness to externalsignaling. To test this, SCNP was used, a multiparametric flowcytometry-based assay that can simultaneously measure both extracellularsurface marker levels and changes in intracellular signaling proteins inresponse to extracellular modulators, quantitatively at the single celllevel (Kornblau et al. Clin Cancer Res 2010).

Methods/Objective:

SCNP was applied to examine baseline and intracellular signalingresponses induced by the extracellular modulators EPO and GCSF in bonemarrow (BM) mononuclear cells (BMMC) derived from healthy donors (n=15)and MDS (n=9) patients. The effects of donor age on signaling profilesin healthy BMMC was compared between samples collected by BM aspiratefrom 6 subjects aged 23-43 years (“younger”) and from the BM present inhip replacement samples from 9 subjects aged 54-82 years (“older”).Signaling profiles were also determined for 9 LR MDS patients aged 53-83years and compared to the age-matched healthy “older” control. Metricsused for analysis included fold change, total phosphorylation levels,and the Mann-Whitney U statistic model.

Results:

There were no differences in the frequency of CD34+ cells (R²=0.006,p=0.78) between “younger” and “older” healthy donor samples. Likewise,there was no age-related difference in functional signaling ability inresponse to GCSF-induced p-STAT1, p-STAT3, & p-STAT5 levels. However,early erythroblasts and normoblasts from older healthy donors weresignificantly less responsive to EPO, as measured by induced phospho(p)-STAT5 levels than those derived from younger healthy donors (e.g.R²=0.654 p=0.008 for erythroblasts and R²=0.628 p=0.0004 fornormoblasts). This suggests that the differences observed in EPOresponse were likely due to donor age rather than sample source.Signaling profiles classified RAEB patients into 2 categories based ondifferences in EPO- and GCSF-induced signaling (FIG. 15). Compared tohealthy age-matched healthy controls, one subset was characterized by ahigh % of RBC precursors (CD45lo nRBC) and increased p-STAT5 levels inresponse to EPO and the other subset by a high % of myeloid cells withrobust GCSF-induced p-STAT3 & p-STAT5 responses in both total myeloidand CD34+ cells. By contrast, patient samples with RARS had a high % ofCD45lo nRBC but lacked robust p-STAT5-induced signaling after modulationwith EPO.

Conclusions:

Overall, these data show the feasibility of using the SCNP assay in BMsamples to functionally characterize signaling pathways simultaneouslyin different cell subsets of healthy donors and patients with MDS. Inhealthy individuals, age-related differences in EPO signaling werediscovered. In LR MDS, differences in signaling were observed betweencases and in comparison to the data from healthy controls. Decipheringsignaling profiles in healthy donor versus MDS patient samples mayresult in improved, biologically-based disease classification thatinforms more effective patient management.

The results of this study and the approach used here have severalapplications. For example, by establishing a normal signaling landscape,some of the functional changes that may occur with age have beenidentified. This normal data set can also be used as a reference foridentifying abnormal responses in diseases such as autoimmune diseases.This approach can be used to monitor changes in the immune system thatoccur after vaccination or with immunotherapy. Finally, this approachcan be used to identify potential therapeutic targets that may allow formodulation of immune responses.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

1. A method comprising: (a) identifying an activation level of one ormore activatable elements in a first cell-type from a test sample; (b)identifying an activation level of the one or more activatable elementsin a second cell-type from a test sample; (c) determining a similarityvalue based on steps a) and step b) and a statistical model, wherein thestatistical model specifies a range of activation levels of one or moreactivatable elements in the first cell-type and the second cell-type ina plurality of normal samples, wherein the statistical model furtherspecifies the variance of the activation levels of the one or moreactivatable elements associated with cells in the plurality of normalsamples.
 2. The method of claim 1, wherein identifying the activationlevel of the one or more activatable comprises: (a) identifying theactivation level of the one or more activatable elements in single cellsderived from the test sample; (b) identifying one or more cell-typemarkers in single cells derived from the test sample; and (c) gatingdiscrete populations of single cells based on the one or more cell-typemarkers associated with the single cells.
 3. The method of claim 1,further comprising generating the statistical model, wherein generatingthe statistical model comprises: (a) identifying the activation level ofthe one or more activatable elements in single cells derived from theplurality of normal samples; (b) identifying one or more cell-typemarkers in single cells derived from the plurality of normal samples;(c) gating cells in the plurality of normal samples based on the one ormore cell-type markers associated with the single cells; and (d)generating the statistical model that specifies the range of activationlevels associated with cells in the normal samples. 4.-5. (canceled) 6.The method of claim 1, further comprising contacting the test sample andthe plurality of normal samples with one or more modulators. 7.-21.(canceled)
 22. The method of claim 1, further comprising administering atherapeutic agent to a subject from whom the test sample is derivedbased on the similarity value. 23.-44. (canceled)
 45. A method ofgenerating a normal cell profile comprising: obtaining a plurality ofsamples of cells from normal individuals, contacting the plurality ofsamples of cells from the normal individuals with one or moremodulators, measuring an activation level of one or more activatableelements in the plurality of samples from the normal individuals, andgenerating a profile, wherein the profile comprises one or more rangesof the activation level of the one or more activatable elements from theplurality of samples of cells from the normal individuals.
 46. Themethod of claim 45, wherein the profile comprises one or more ranges ofactivation levels of the one or more activatable elements that exhibitvariance of less than 50% among normal samples. 47.-53. (canceled) 54.The method of claim 45, further comprising displaying the activationlevel of the one or more activatable elements from the plurality ofsamples of cells from normal individuals in a report. 55.-60. (canceled)61. A method comprising: (a) measuring an activation level of one ormore activatable elements from cells from a test sample from a subject;(b) comparing the activation level of the one or more activatableelements from cells from the test sample to a model, wherein the modelis derived from determining a range of activation levels of one or moreactivatable elements from samples of cells from a plurality of normalindividuals; and (c) preparing a report displaying the activation levelof the one or activatable elements from the samples of cells from theplurality of normal individuals to the activation level of the one ormore activatable elements from cells from the test sample from thesubject. 62.-64. (canceled)
 65. The method of claim 61, wherein thesamples of cells from a plurality of normal individuals were contactedwith one or more modulators
 66. The method of claim 65 furthercomprising contacting the plurality of samples of cells from the testsample from the subject with the one or more modulators. 67.-77.(canceled)
 78. The method of claim 61, further comprising making aclinical decision based on said comparing.
 79. The method of claim 78,wherein the clinical decision comprises a diagnosis, prognosis, ormonitoring the subject.
 80. The method of claim 61, further comprisingproviding the report to a healthcare provider.
 81. The method of claim61, further comprising providing the report to the subject. 82.(canceled)
 83. A report comprising a visual representation ofmultiparametric results of a test sample, the visual representationcomprising a comparison between an activation level of two or moreactivatable elements in single cells from a test sample and a range ofactivation levels of the two or more activatable elements in singlecells in a plurality of samples used as a standard.
 84. The report ofclaim 83, further comprising a statistical model, wherein thestatistical model specifies the range of activation levels of the two ormore activatable elements in single cells in a plurality of samples usedas a standard.
 85. The report of claim 84, further comprising asimilarity value between the activation level of the two or moreactivatable elements in single cells from a test sample and thestatistical model.
 86. The report of claim 83, further comprising ascatterplot, a line graph with error bars, a histogram, a bar andwhisker plot, a circle plot, a radar plot, a heat map, and/or a bargraph.
 87. The report of claim 83, wherein a computer server generatesthe report.
 88. The report of claim 83, wherein the report comprisesinformation on cell growth, cell survival and/or cytostasis.
 89. Thereport of claim 83, wherein the two or more activatable elementscomprise two or more proteins. 90.-94. (canceled)